I've Been Through The Desert With No Internet To My Name

America's Internet Deserts, and Future Possibilities If You're In One

This dataset is from Kaggle and contains information from the 2016 American Community Survey. It includes ethnographic, education and economic data, as well as data about people without internet by state and county. The dataset is robust, and provides additional demographic information such as median age, median income, and median rent. However, it does not have data from many counties. While this is problematic, the American Community Survey does exclude data if it is not statistically significant. Despite this, the dataset is still worth considering, since it comprises 820 counties of 3,000.

This is an interesting dataset to consider because people may often think internet accessibility to be ubiquitous, but for a myriad of reasons it is not. This data is obtained through the question “Does your household have a broadband internet subscription?” While this does not account for access through devices like cell phones and tablets with 4G for example, it does still demonstrate some potential concerns if a community has very high rates of households without internet access.

I would posit after considering the data that communities that have more people without internet will have lower educational attainment and higher rates of poverty. As the internet becomes less of a luxury, and more of a necessity, it will be imperative that these communities have better access to internet, to encourage higher quality education and access to helpful and potentially lifesaving information. It will first be important to learn what the average percentage of persons without internet is and determine the standard deviation. This will make it easy to understand which communities have exceptionally good access to internet, and which do not. Much like there are food deserts, are there “internet deserts” as well? If so, where are they? And is there a correlation to education or income?

In [2]:
#Since the warnings were not problematic, I suppressed them. 
import warnings
warnings.filterwarnings('ignore')
#standard imports
import numpy as np
import pandas as pd
import os
#matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib.pyplot import figure
#plotly
import plotly.plotly as py
from plotly.graph_objs import *
import plotly
import plotly.tools as tls
import plotly.graph_objs as go
import plotly.figure_factory as ff
from  plotly  import __version__
#plotly offline
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
print(__version__) # requires version >= 1.9.0
init_notebook_mode(connected=True)
#scikitlearn
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
import sklearn
sklearn.__version__
from sklearn import datasets, linear_model
# Scientific libraries
from numpy import arange,array,ones
from scipy import stats
3.1.0

The Dataset

Below, you will find the dataset that I used in this project. It contains 820 rows and 23 columns and lists the county and state, as well as education, median income for the county, population of people in the county below the poverty line, and percent of people without internet access in that county. The educational data is listed by the number of people with that education level. The ethnographic information is also listed by the number of persons that identified as that race in the county. You may notice the GEOID as well. This is an identifier used by the US Government to give a unique code to each county. I had to clean up the data to avoid type errors, and maintained the original dataset as df.

In [3]:
#Read in dataset without truncation
df = pd.read_csv('~/Desktop/Python Exercises/kaggle_internet.csv')
pd.set_option('display.max_rows', 820)
pd.set_option('display.max_columns', 23)
df
Out[3]:
county state GEOID lon lat P_total P_white P_black P_asian P_native P_hawaiian P_others P_below_middle_school P_some_high_school P_high_school_equivalent P_some_college P_bachelor_and_above P_below_poverty median_age gini_index median_household_income median_rent_per_income percent_no_internet
0 Anchorage Municipality AK 05000US02020 -149.274354 61.177549 298192 184841.0 16102.0 27142.0 23916.0 7669.0 7935.0 2234.0 8196.0 44804.0 66162.0 70713.0 18302 33.0 0.4018 85634 28.0 6.593887
1 Fairbanks North Star Borough AK 05000US02090 -146.599867 64.690832 100605 75501.0 4385.0 3875.0 7427.0 503.0 2357.0 924.0 1527.0 14725.0 24570.0 19257.0 9580 30.6 0.3756 77328 25.6 12.102458
2 Matanuska-Susitna Borough AK 05000US02170 -149.407974 62.182173 104365 86314.0 1019.0 1083.0 5455.0 141.0 325.0 337.0 2755.0 21071.0 28472.0 12841.0 9893 34.2 0.4351 69332 29.6 11.156575
3 Baldwin County AL 05000US01003 -87.746067 30.659218 208563 180484.0 18821.0 914.0 1383.0 0.0 1469.0 3245.0 10506.0 41822.0 46790.0 43547.0 23375 42.4 0.4498 56732 29.3 17.868167
4 Calhoun County AL 05000US01015 -85.822513 33.771706 114611 NaN NaN NaN NaN NaN NaN 2455.0 8853.0 24761.0 26625.0 12909.0 18193 39.1 0.4692 41687 24.8 23.464932
5 Cullman County AL 05000US01043 -86.869267 34.131923 82471 NaN NaN NaN NaN NaN NaN 3273.0 8398.0 18481.0 16268.0 9732.0 11524 40.4 0.4518 39411 29.7 23.294498
6 DeKalb County AL 05000US01049 -85.803992 34.460929 70900 NaN NaN NaN NaN NaN NaN 2608.0 7356.0 15325.0 15319.0 5243.0 15029 39.8 0.4528 35963 31.4 28.720009
7 Elmore County AL 05000US01051 -86.142739 32.597229 81799 NaN NaN NaN NaN NaN NaN 835.0 5215.0 17016.0 17118.0 14645.0 11283 38.3 0.4535 52579 33.6 13.805792
8 Etowah County AL 05000US01055 -86.034420 34.047638 102564 NaN NaN NaN NaN NaN NaN 2880.0 8685.0 24731.0 22151.0 11075.0 16955 41.2 0.4477 41152 26.1 19.155961
9 Houston County AL 05000US01069 -85.296398 31.158193 104056 71838.0 27762.0 1054.0 443.0 0.0 1031.0 1698.0 8156.0 21501.0 24051.0 14517.0 20571 39.5 0.4799 42321 29.4 25.738504
10 Jefferson County AL 05000US01073 -86.896536 33.553444 659521 340506.0 279979.0 8366.0 1799.0 323.0 14127.0 7423.0 35046.0 117923.0 135554.0 147831.0 97105 38.0 0.5145 50180 29.9 18.017671
11 Lauderdale County AL 05000US01077 -87.650997 34.904122 92318 NaN NaN NaN NaN NaN NaN 1678.0 4841.0 22912.0 17777.0 13815.0 12965 41.6 0.4424 43427 25.3 23.669967
12 Lee County AL 05000US01081 -85.353048 32.604064 158991 NaN NaN NaN NaN NaN NaN 2683.0 7493.0 21014.0 28516.0 33905.0 29192 31.0 0.4773 48056 29.3 13.170132
13 Limestone County AL 05000US01083 -86.981399 34.810239 92753 NaN NaN NaN NaN NaN NaN 2061.0 8374.0 19322.0 18124.0 14228.0 11995 38.8 0.4602 50872 27.9 16.643664
14 Madison County AL 05000US01089 -86.551080 34.764238 356967 243688.0 87415.0 8383.0 2547.0 250.0 4835.0 5829.0 14080.0 55090.0 66161.0 98936.0 49174 38.8 0.4803 60503 29.2 11.749229
15 Marshall County AL 05000US01095 -86.321668 34.309564 95157 NaN NaN NaN NaN NaN NaN 3284.0 7053.0 21479.0 21168.0 10531.0 21870 38.0 0.4788 42362 29.4 19.308299
16 Mobile County AL 05000US01097 -88.196568 30.684573 414836 243284.0 148001.0 8269.0 3328.0 20.0 4702.0 4425.0 29557.0 89541.0 85667.0 65919.0 78468 37.4 0.4756 45744 31.1 22.561903
17 Montgomery County AL 05000US01101 -86.203831 32.203651 226349 81740.0 130006.0 5753.0 339.0 36.0 3774.0 3958.0 15674.0 36639.0 44409.0 48055.0 39278 36.4 0.5013 45395 32.4 18.443855
18 Morgan County AL 05000US01103 -86.846402 34.454484 119012 93591.0 14431.0 1076.0 763.0 0.0 6454.0 3238.0 9033.0 24468.0 25545.0 16219.0 19679 40.7 0.4814 44378 23.2 24.689843
19 St. Clair County AL 05000US01115 -86.315663 33.712963 88019 NaN NaN NaN NaN NaN NaN 1352.0 7856.0 22149.0 17659.0 10665.0 8971 40.3 0.3772 60158 28.0 17.188044
20 Shelby County AL 05000US01117 -86.678104 33.262937 210622 168484.0 24066.0 4535.0 211.0 197.0 8566.0 1709.0 7885.0 27924.0 43895.0 60429.0 16706 38.9 0.4305 73647 24.3 8.630142
21 Talladega County AL 05000US01121 -86.175804 33.369277 80103 NaN NaN NaN NaN NaN NaN 1484.0 7572.0 18221.0 19081.0 8402.0 12706 40.8 0.4510 39393 28.2 22.032554
22 Tuscaloosa County AL 05000US01125 -87.522860 33.290202 206102 NaN NaN NaN NaN NaN NaN 2513.0 10441.0 35820.0 39734.0 38466.0 34792 32.9 0.4748 47787 32.9 19.624788
23 Walker County AL 05000US01127 -87.301092 33.791571 64967 NaN NaN NaN NaN NaN NaN 1964.0 5733.0 17432.0 13423.0 5016.0 13919 41.8 0.4380 39068 31.8 23.794158
24 Benton County AR 05000US05007 -94.256187 36.337825 258291 228649.0 3896.0 9343.0 2656.0 1350.0 3509.0 7268.0 9534.0 49236.0 45237.0 54165.0 22874 35.4 0.4329 63631 22.2 14.930048
25 Craighead County AR 05000US05031 -90.630411 35.828268 105835 NaN NaN NaN NaN NaN NaN 1942.0 4798.0 25343.0 18204.0 15874.0 16602 34.5 0.5027 43678 27.0 16.348931
26 Faulkner County AR 05000US05045 -92.324654 35.146356 122227 100295.0 12771.0 1404.0 277.0 739.0 2809.0 900.0 4581.0 23926.0 22803.0 21557.0 22285 33.1 0.4724 48506 29.3 15.878901
27 Garland County AR 05000US05051 -93.146915 34.578861 97477 NaN NaN NaN NaN NaN NaN 913.0 6475.0 20535.0 27843.0 12701.0 17824 44.0 0.4441 42826 30.9 17.736396
28 Jefferson County AR 05000US05069 -91.930701 34.277696 70016 NaN NaN NaN NaN NaN NaN 1913.0 5724.0 16087.0 13032.0 8694.0 14565 39.0 0.4748 37712 28.6 31.988785
29 Lonoke County AR 05000US05085 -91.894132 34.755114 72228 63910.0 3884.0 1032.0 292.0 0.0 1309.0 548.0 3251.0 16853.0 16004.0 10186.0 10548 36.4 0.3900 55837 26.2 19.633836
30 Pulaski County AR 05000US05119 -92.316515 34.773988 393250 218346.0 142430.0 8604.0 799.0 33.0 9119.0 4258.0 15988.0 68957.0 82502.0 90368.0 68881 36.8 0.5132 47387 29.1 19.187059
31 Saline County AR 05000US05125 -92.674463 34.648525 118703 NaN NaN NaN NaN NaN NaN 838.0 5217.0 26651.0 25394.0 23248.0 8802 41.0 0.3820 64932 24.2 12.353877
32 Sebastian County AR 05000US05131 -94.274989 35.196981 127793 93101.0 8645.0 5547.0 1893.0 74.0 13978.0 4867.0 7636.0 29228.0 28434.0 14897.0 21913 37.8 0.4583 42053 26.9 19.473117
33 Washington County AR 05000US05143 -94.218417 35.971209 228049 174457.0 8608.0 5402.0 2189.0 4953.0 25446.0 7095.0 9994.0 35733.0 36425.0 44638.0 36641 31.5 0.4917 45679 28.5 15.143759
34 White County AR 05000US05145 -91.753158 35.254722 79263 NaN NaN NaN NaN NaN NaN 626.0 4532.0 20224.0 13896.0 11030.0 10941 35.8 0.4236 42844 30.7 23.751816
35 Apache County AZ 05000US04001 -109.493747 35.385845 73112 16162.0 112.0 106.0 53520.0 54.0 694.0 3238.0 4230.0 14071.0 17542.0 5263.0 24122 34.6 0.4892 34685 17.3 54.011390
36 Cochise County AZ 05000US04003 -109.754120 31.881793 125770 109899.0 5077.0 2783.0 1875.0 434.0 1913.0 3800.0 6377.0 22897.0 31858.0 21340.0 26604 40.8 0.4325 45508 27.1 17.925300
37 Coconino County AZ 05000US04005 -111.773728 35.829692 140908 91167.0 2166.0 2670.0 37010.0 163.0 2711.0 1896.0 5541.0 18074.0 26263.0 28973.0 22902 30.9 0.4525 55091 30.4 18.389301
38 Maricopa County AZ 05000US04013 -112.495533 33.346541 4242997 3227510.0 230642.0 168720.0 82300.0 9808.0 371556.0 123066.0 193976.0 629861.0 922027.0 896358.0 626082 36.2 0.4699 58737 29.0 12.044591
39 Mohave County AZ 05000US04015 -113.749689 35.717705 205249 187492.0 1614.0 2029.0 4870.0 231.0 3539.0 3077.0 18637.0 53416.0 60615.0 17523.0 35646 50.6 0.4645 42423 28.2 16.624252
40 Navajo County AZ 05000US04017 -110.320908 35.390934 110026 50543.0 755.0 897.0 49003.0 0.0 4536.0 2938.0 8116.0 21081.0 26042.0 10173.0 30844 36.3 0.4847 36998 27.8 36.002025
41 Pima County AZ 05000US04019 -111.783018 32.128237 1016206 774608.0 34344.0 28713.0 32670.0 1550.0 94614.0 21541.0 48684.0 149691.0 230298.0 215628.0 181277 38.3 0.4684 47560 30.3 12.641611
42 Pinal County AZ 05000US04021 -111.367257 32.918910 418540 337571.0 18679.0 8568.0 21796.0 1357.0 17937.0 8960.0 26252.0 80736.0 106961.0 58235.0 61143 39.2 0.4407 52555 29.2 13.110493
43 Yavapai County AZ 05000US04025 -112.573745 34.630044 225562 209960.0 1130.0 2368.0 3491.0 207.0 4025.0 1506.0 9954.0 47109.0 70385.0 41400.0 28082 53.1 0.4627 50420 30.4 17.079463
44 Yuma County AZ 05000US04027 -113.910905 32.773942 205631 112651.0 3696.0 2719.0 2926.0 266.0 78192.0 14696.0 14229.0 34499.0 41773.0 20023.0 36136 34.8 0.4269 43518 27.0 18.006897
45 Alameda County CA 05000US06001 -121.913304 37.648081 1647704 688067.0 174855.0 483856.0 10122.0 13865.0 171152.0 63504.0 63957.0 203437.0 287522.0 526093.0 172923 37.4 0.4604 89979 29.8 10.138773
46 Butte County CA 05000US06007 -121.601919 39.665959 226864 187892.0 3563.0 10665.0 2567.0 811.0 9577.0 5192.0 10017.0 34807.0 58155.0 38076.0 43838 38.1 0.4888 45177 38.5 13.589225
47 Contra Costa County CA 05000US06013 -121.951543 37.919479 1135127 658852.0 97413.0 183849.0 3159.0 4891.0 104400.0 37361.0 35488.0 133598.0 239017.0 323443.0 97049 39.5 0.4596 91045 31.1 7.169144
48 El Dorado County CA 05000US06017 -120.534398 38.785532 185625 162495.0 1795.0 8904.0 2379.0 462.0 3745.0 3030.0 5222.0 29859.0 49951.0 45596.0 15373 46.1 0.4609 75772 29.2 10.588202
49 Fresno County CA 05000US06019 -119.655019 36.761006 979915 640619.0 47646.0 101993.0 10176.0 1183.0 143649.0 70135.0 64384.0 139362.0 187031.0 120826.0 247507 32.1 0.4910 48715 36.5 17.471044
50 Humboldt County CA 05000US06023 -123.925818 40.706673 136646 110795.0 1172.0 4212.0 7228.0 0.0 3154.0 991.0 6924.0 23705.0 32405.0 27020.0 26945 37.9 0.5038 43130 35.3 14.925318
51 Imperial County CA 05000US06025 -115.355395 33.040816 180883 110971.0 4122.0 2731.0 1808.0 301.0 54515.0 13792.0 16748.0 26038.0 34525.0 15162.0 42303 32.5 0.4746 49095 31.8 22.574964
52 Kern County CA 05000US06029 -118.729506 35.346629 884788 642077.0 47668.0 40615.0 11936.0 3142.0 106937.0 59354.0 68973.0 144166.0 162350.0 86885.0 193133 31.3 0.4644 49903 32.7 17.331400
53 Kings County CA 05000US06031 -119.815530 36.072478 149785 99706.0 10028.0 5238.0 2083.0 472.0 25665.0 8953.0 12668.0 28265.0 29719.0 10671.0 21565 31.8 0.4321 53234 28.4 17.296356
54 Lake County CA 05000US06033 -122.746757 39.094802 64116 NaN NaN NaN NaN NaN NaN 1948.0 4675.0 11805.0 20786.0 7617.0 12973 46.5 0.4763 42029 34.2 23.104135
55 Los Angeles County CA 05000US06037 -118.261862 34.196398 10137915 5093898.0 826516.0 1474575.0 65370.0 26104.0 2258451.0 744735.0 604335.0 1433558.0 1787322.0 2167313.0 1628305 36.3 0.5030 61338 34.5 14.209510
56 Madera County CA 05000US06039 -119.749852 37.210039 154697 109705.0 4535.0 2748.0 3262.0 312.0 28121.0 13445.0 11722.0 24199.0 32648.0 11339.0 29736 33.6 0.4481 51657 33.7 20.297956
57 Marin County CA 05000US06041 -122.745974 38.051817 260651 200426.0 6114.0 13886.0 274.0 397.0 25607.0 8411.0 3776.0 19769.0 44814.0 112386.0 19762 46.1 0.5245 103845 30.5 7.208487
58 Mendocino County CA 05000US06045 -123.442881 39.432388 87628 71202.0 412.0 1125.0 3169.0 231.0 5887.0 3776.0 5269.0 15184.0 22807.0 13734.0 16881 42.8 0.5156 43809 37.4 16.275854
59 Merced County CA 05000US06047 -120.722802 37.194806 268672 149377.0 8492.0 21156.0 2522.0 32.0 77857.0 26343.0 20093.0 38469.0 47303.0 21342.0 53314 30.9 0.4969 47739 29.7 15.371945
60 Monterey County CA 05000US06053 -121.315573 36.240107 435232 242774.0 11055.0 24852.0 1903.0 1086.0 135093.0 45225.0 24535.0 52379.0 72620.0 68343.0 52257 34.0 0.4514 63876 32.0 17.628440
61 Napa County CA 05000US06055 -122.325995 38.507351 142166 110943.0 2718.0 11301.0 1160.0 269.0 10439.0 7457.0 5699.0 17316.0 32157.0 34822.0 10032 41.0 0.4641 75077 29.9 11.858114
62 Nevada County CA 05000US06057 -120.773446 39.295191 99107 91701.0 197.0 1742.0 490.0 0.0 1414.0 773.0 4665.0 12973.0 29321.0 27690.0 10505 50.2 0.4784 59022 34.3 11.390410
63 Orange County CA 05000US06059 -117.777207 33.675687 3172532 1986194.0 53602.0 637622.0 13443.0 10791.0 352857.0 148793.0 145823.0 369136.0 600961.0 862800.0 346002 37.7 0.4695 81837 33.1 7.381774
64 Placer County CA 05000US06061 -120.722718 39.062032 380531 309901.0 5796.0 26369.0 2292.0 392.0 15303.0 4001.0 7848.0 46015.0 103240.0 104141.0 28064 41.9 0.4571 85426 30.0 8.078847
65 Riverside County CA 05000US06065 -116.002239 33.729827 2387741 1406044.0 152433.0 149105.0 20442.0 7583.0 544724.0 120739.0 145309.0 402375.0 506515.0 335035.0 360386 35.3 0.4559 60134 34.2 12.419725
66 Sacramento County CA 05000US06067 -121.340441 38.450011 1514460 882763.0 148706.0 230645.0 10265.0 16214.0 117768.0 56814.0 66821.0 230687.0 345423.0 302855.0 245111 36.0 0.4649 59780 32.5 11.045058
67 San Bernardino County CA 05000US06071 -116.181197 34.857220 2140096 1314742.0 182864.0 152270.0 15077.0 6381.0 364338.0 101683.0 157577.0 351679.0 433564.0 267503.0 368528 33.2 0.4400 56337 34.3 12.132071
68 San Diego County CA 05000US06073 -116.776117 33.023604 3317749 2385470.0 165878.0 389369.0 24980.0 14356.0 169165.0 126358.0 148766.0 411111.0 684874.0 835451.0 398475 35.7 0.4644 70824 33.7 9.549978
69 San Francisco County CA 05000US06075 -123.032229 37.727239 870887 404410.0 43468.0 300409.0 3054.0 1967.0 70599.0 40123.0 32703.0 84652.0 126771.0 394813.0 86900 38.0 0.5029 103801 24.6 10.034485
70 San Joaquin County CA 05000US06077 -121.272237 37.935034 733709 417952.0 52126.0 111547.0 5428.0 4697.0 74430.0 43819.0 51513.0 126697.0 151553.0 77034.0 103399 34.3 0.4466 59518 31.6 16.190698
71 San Luis Obispo County CA 05000US06079 -120.447540 35.385227 282887 238692.0 5167.0 10989.0 2262.0 28.0 16126.0 5377.0 8615.0 35036.0 70091.0 68286.0 28364 38.8 0.4376 70564 29.2 10.878358
72 San Mateo County CA 05000US06081 -122.371542 37.414664 764797 388724.0 17413.0 214801.0 3102.0 10756.0 85183.0 29943.0 24778.0 84004.0 128261.0 275493.0 49373 39.5 0.4821 108627 29.5 7.291085
73 Santa Barbara County CA 05000US06083 -120.038485 34.537378 446170 341741.0 8202.0 24802.0 4863.0 94.0 46374.0 32195.0 17569.0 47780.0 84230.0 89313.0 59709 33.8 0.4707 67436 34.5 10.924612
74 Santa Clara County CA 05000US06085 -121.690622 37.220777 1919402 844820.0 47142.0 683532.0 8787.0 8602.0 227352.0 79892.0 68559.0 198127.0 280826.0 673605.0 177431 37.0 0.4645 111069 29.3 6.782741
75 Santa Cruz County CA 05000US06087 -122.007205 37.012488 274673 203973.0 2908.0 13692.0 1797.0 43.0 40674.0 13839.0 6871.0 28049.0 55684.0 72551.0 36169 37.4 0.4857 77613 35.3 9.528658
76 Shasta County CA 05000US06089 -122.043550 40.760522 179631 158293.0 1791.0 6016.0 2942.0 77.0 1522.0 1943.0 7170.0 31960.0 56179.0 27576.0 30786 42.1 0.4655 46724 34.7 17.224495
77 Solano County CA 05000US06095 -121.939594 38.267226 440207 219084.0 64438.0 66560.0 1568.0 3713.0 46315.0 13663.0 20668.0 70351.0 117183.0 76316.0 49802 37.9 0.4340 73900 30.7 8.367522
78 Sonoma County CA 05000US06097 -122.945194 38.532574 503070 371195.0 8632.0 21487.0 4397.0 1542.0 71200.0 19314.0 21317.0 66982.0 127657.0 122082.0 45561 42.1 0.4481 73929 32.9 9.903255
79 Stanislaus County CA 05000US06099 -121.002656 37.562384 541560 395715.0 17096.0 30615.0 3427.0 3166.0 68689.0 35558.0 36338.0 94190.0 111719.0 55620.0 76191 34.0 0.4532 54305 34.4 15.160139
80 Sutter County CA 05000US06101 -121.702758 39.035257 96651 69300.0 1672.0 14849.0 653.0 501.0 3331.0 6649.0 6252.0 13546.0 23334.0 9987.0 17985 35.9 0.4662 51397 31.3 16.537332
81 Tulare County CA 05000US06107 -118.780542 36.230453 460437 340284.0 6167.0 20506.0 6233.0 908.0 72610.0 37578.0 35771.0 69771.0 80388.0 36919.0 114290 30.8 0.4554 45881 32.1 18.494191
82 Ventura County CA 05000US06111 -119.133143 34.358742 849738 681115.0 14929.0 60772.0 5746.0 1787.0 45523.0 48163.0 35936.0 105220.0 181595.0 190429.0 79392 37.7 0.4474 80135 34.0 12.131702
83 Yolo County CA 05000US06113 -121.903178 38.679268 215802 151935.0 5476.0 30155.0 1835.0 836.0 11635.0 8756.0 9417.0 22892.0 33335.0 52163.0 41907 30.9 0.5057 64904 31.7 9.594141
84 Yuba County CA 05000US06115 -121.344280 39.270026 75275 56042.0 2331.0 3656.0 1046.0 1376.0 3971.0 1692.0 4688.0 10799.0 20110.0 8735.0 10921 32.4 0.4444 49259 32.4 15.172853
85 Adams County CO 05000US08001 -104.331872 39.874325 498187 402970.0 15865.0 19783.0 7677.0 425.0 32370.0 19922.0 29749.0 92677.0 97658.0 73008.0 57822 33.7 0.4006 66033 32.2 13.087137
86 Arapahoe County CO 05000US08005 -104.331733 39.644632 637068 452406.0 69336.0 38563.0 3623.0 1618.0 47841.0 9568.0 19604.0 94113.0 125857.0 178155.0 56327 36.5 0.4538 70950 31.0 8.686033
87 Boulder County CO 05000US08013 -105.398382 40.094826 322226 288623.0 3453.0 15560.0 927.0 209.0 5652.0 3054.0 6392.0 27928.0 44746.0 127679.0 34157 36.4 0.4840 74615 34.7 6.382652
88 Broomfield County CO 05000US08014 -105.052125 39.953383 66529 57480.0 727.0 3943.0 316.0 0.0 1812.0 NaN NaN NaN NaN NaN 5643 38.4 0.4614 84349 27.4 4.408238
89 Denver County CO 05000US08031 -104.880625 39.761849 693060 522284.0 68262.0 25721.0 5462.0 410.0 47322.0 20305.0 34126.0 89883.0 106830.0 234447.0 93188 34.4 0.5008 61105 30.4 11.419746
90 Douglas County CO 05000US08035 -104.926199 39.326435 328632 294600.0 4667.0 15524.0 1294.0 205.0 3739.0 1081.0 1920.0 24611.0 59646.0 128220.0 12037 38.9 0.4071 109292 29.1 2.660562
91 El Paso County CO 05000US08041 -104.527472 38.827383 688284 552441.0 43539.0 18301.0 3588.0 3009.0 25506.0 5902.0 16118.0 87447.0 158713.0 170871.0 76464 33.9 0.4321 63882 29.9 6.932988
92 Jefferson County CO 05000US08059 -105.245600 39.586459 571837 519734.0 7314.0 17174.0 3596.0 273.0 9742.0 4368.0 16307.0 81575.0 125690.0 178031.0 37593 40.2 0.4323 74186 32.1 6.587986
93 Larimer County CO 05000US08069 -105.482131 40.663091 339993 307362.0 3351.0 7489.0 2714.0 516.0 6735.0 1364.0 5554.0 46045.0 63903.0 103892.0 37699 35.9 0.4400 66469 34.2 7.874771
94 Mesa County CO 05000US08077 -108.461837 39.019492 150083 140112.0 465.0 1263.0 1384.0 467.0 2111.0 2159.0 8867.0 30598.0 33431.0 25198.0 23494 39.8 0.4346 48846 33.6 10.453911
95 Pueblo County CO 05000US08101 -104.489893 38.170658 165123 133330.0 4192.0 1310.0 8264.0 127.0 13092.0 1670.0 10044.0 31543.0 43130.0 23759.0 32757 39.1 0.4477 44677 32.8 18.726616
96 Weld County CO 05000US08123 -104.383649 40.555794 294932 271892.0 3435.0 4523.0 970.0 0.0 6937.0 6670.0 14259.0 54291.0 58987.0 51217.0 34393 34.3 0.4375 63400 31.5 11.473547
97 Fairfield County CT 05000US09001 -73.366757 41.228103 944177 696692.0 110693.0 50381.0 2811.0 695.0 52428.0 23085.0 29891.0 141427.0 136932.0 297908.0 79372 40.4 0.5404 90123 32.7 10.109508
98 Hartford County CT 05000US09003 -72.732916 41.806053 892389 636425.0 120762.0 45365.0 2763.0 82.0 57288.0 17405.0 37350.0 173731.0 149818.0 230416.0 94580 40.6 0.4695 69433 30.0 14.885449
99 Litchfield County CT 05000US09005 -73.235428 41.791897 182571 NaN NaN NaN NaN NaN NaN 1661.0 5935.0 39025.0 37076.0 49581.0 12350 46.8 0.4497 76993 29.7 9.964240
100 Middlesex County CT 05000US09007 -72.524227 41.434525 163329 145795.0 8763.0 4893.0 5.0 0.0 791.0 1806.0 3504.0 32210.0 30049.0 49292.0 14077 45.6 0.4671 79739 28.6 12.848096
101 New Haven County CT 05000US09009 -72.900204 41.349717 856875 635115.0 110901.0 35579.0 1827.0 423.0 45612.0 15228.0 36959.0 181219.0 142740.0 208217.0 92957 39.9 0.4617 66176 30.9 13.932715
102 New London County CT 05000US09011 -72.103452 41.478630 269801 218961.0 15524.0 12084.0 1730.0 59.0 9528.0 1828.0 9560.0 57606.0 55381.0 60961.0 23140 41.5 0.4591 70699 27.9 11.652222
103 Tolland County CT 05000US09013 -72.340977 41.858076 151118 NaN NaN NaN NaN NaN NaN 535.0 3382.0 26120.0 25910.0 37892.0 9052 37.7 0.4237 81252 32.8 11.576163
104 Windham County CT 05000US09015 -71.990702 41.824999 116192 103169.0 3606.0 1806.0 555.0 0.0 2263.0 1479.0 6841.0 27438.0 23532.0 19321.0 13600 40.8 0.4283 61608 27.9 15.809004
105 District of Columbia DC 05000US11001 -77.017094 38.904148 681170 277268.0 320554.0 26436.0 2004.0 41.0 37132.0 12245.0 28646.0 85248.0 76292.0 272875.0 120308 33.9 0.5420 75506 29.3 17.649631
106 Kent County DE 05000US10001 -75.502982 39.097088 174827 115964.0 43984.0 3125.0 961.0 9.0 2543.0 2624.0 9704.0 41865.0 33408.0 26391.0 23384 37.5 0.4152 54140 30.6 13.386706
107 New Castle County DE 05000US10003 -75.644132 39.575915 556987 361605.0 138491.0 29993.0 1877.0 981.0 10592.0 5568.0 22838.0 120004.0 93543.0 135437.0 60983 38.5 0.4599 67274 29.9 10.673170
108 Sussex County DE 05000US10005 -75.335495 38.677511 220251 181522.0 27436.0 2528.0 1217.0 1063.0 2460.0 4109.0 15705.0 50605.0 47459.0 43241.0 23844 48.7 0.4418 57734 29.4 19.165731
109 Alachua County FL 05000US12001 -82.357221 29.675740 263496 182119.0 52833.0 16798.0 1166.0 524.0 2594.0 3444.0 8861.0 36949.0 44967.0 63363.0 58215 31.3 0.5081 45304 37.8 13.339264
110 Bay County FL 05000US12005 -85.631348 30.237563 183974 151108.0 20489.0 5077.0 317.0 34.0 1066.0 1639.0 8458.0 42062.0 45237.0 29940.0 26569 40.1 0.4489 49157 31.8 13.039955
111 Brevard County FL 05000US12009 -80.700384 28.298275 579130 478586.0 57238.0 14164.0 1254.0 170.0 10763.0 6810.0 23736.0 121316.0 147381.0 125850.0 86137 47.3 0.4645 51184 30.7 10.627774
112 Broward County FL 05000US12011 -80.476658 26.193520 1909632 1163610.0 545324.0 69401.0 5993.0 1726.0 63148.0 53722.0 91244.0 360675.0 408687.0 415410.0 254901 40.3 0.4902 54212 36.2 12.772608
113 Charlotte County FL 05000US12015 -81.940858 26.868826 178465 160324.0 11548.0 1660.0 124.0 514.0 1328.0 3253.0 10503.0 47595.0 48932.0 34421.0 21977 58.5 0.4792 44200 33.3 12.395704
114 Citrus County FL 05000US12017 -82.524796 28.843628 143621 NaN NaN NaN NaN NaN NaN 1331.0 11217.0 42315.0 34882.0 23638.0 23564 56.4 0.4443 39206 33.0 16.313049
115 Clay County FL 05000US12019 -81.858147 29.987116 208311 166888.0 21159.0 5746.0 51.0 397.0 4650.0 1814.0 6739.0 48783.0 49991.0 33323.0 20160 40.4 0.4168 56315 34.1 9.375422
116 Collier County FL 05000US12021 -81.400884 26.118713 365136 321423.0 26142.0 4794.0 987.0 0.0 7911.0 17658.0 18496.0 71481.0 69551.0 93512.0 41160 50.3 0.5327 61228 32.5 12.127836
117 Columbia County FL 05000US12023 -82.623127 30.221305 69299 NaN NaN NaN NaN NaN NaN 863.0 5202.0 17142.0 15753.0 6442.0 9440 41.2 0.4855 42019 27.9 15.341730
118 Duval County FL 05000US12031 -81.648113 30.335245 926255 560080.0 271278.0 39936.0 2013.0 453.0 16293.0 10395.0 50513.0 171527.0 203635.0 185836.0 130321 36.1 0.4541 51980 28.8 14.515774
119 Escambia County FL 05000US12033 -87.339040 30.611664 315187 216766.0 70316.0 9687.0 1608.0 96.0 3712.0 2627.0 14573.0 56229.0 78400.0 56508.0 41672 37.3 0.4301 44788 31.1 13.014945
120 Flagler County FL 05000US12035 -81.286362 29.474894 108310 NaN NaN NaN NaN NaN NaN 1624.0 5592.0 26324.0 29474.0 18914.0 12404 51.1 0.4246 49395 32.4 14.734574
121 Hernando County FL 05000US12053 -82.464835 28.567911 182835 162269.0 9823.0 2262.0 210.0 0.0 2822.0 3249.0 12006.0 48935.0 44967.0 25711.0 28156 49.0 0.4256 47253 34.7 15.050766
122 Highlands County FL 05000US12055 -81.340921 27.342627 100917 NaN NaN NaN NaN NaN NaN 3128.0 5618.0 33915.0 22021.0 10022.0 18397 54.4 0.4504 36490 34.5 26.144094
123 Hillsborough County FL 05000US12057 -82.349568 27.906590 1376238 971753.0 228361.0 54822.0 4763.0 1279.0 68366.0 32937.0 66969.0 260737.0 260006.0 301728.0 204523 37.0 0.4789 54588 30.5 11.092797
124 Indian River County FL 05000US12061 -80.574803 27.700638 151563 129043.0 11560.0 1978.0 459.0 0.0 3617.0 4170.0 8384.0 32502.0 33662.0 32393.0 17918 53.0 0.5373 49072 32.5 14.886247
125 Lake County FL 05000US12069 -81.712282 28.764113 335396 283513.0 35729.0 6718.0 1217.0 27.0 1418.0 4904.0 17742.0 83061.0 80571.0 56517.0 38311 46.7 0.4094 50226 30.4 12.773105
126 Lee County FL 05000US12071 -81.892250 26.552134 722336 615028.0 60475.0 11131.0 1457.0 129.0 20665.0 19783.0 40922.0 164285.0 155114.0 152967.0 91858 48.2 0.4717 52909 32.0 13.672608
127 Leon County FL 05000US12073 -84.277800 30.459310 287822 177676.0 88212.0 10203.0 521.0 256.0 2971.0 1960.0 7531.0 31491.0 47093.0 81023.0 53318 30.8 0.4771 51107 33.1 7.597285
128 Manatee County FL 05000US12081 -82.365783 27.481386 375888 320089.0 33480.0 7143.0 1015.0 206.0 7070.0 7561.0 21458.0 87221.0 82575.0 76974.0 47005 47.7 0.4784 51748 34.0 13.976766
129 Marion County FL 05000US12083 -82.043100 29.202805 349020 286955.0 44143.0 4308.0 194.0 122.0 1951.0 5874.0 24091.0 99529.0 78526.0 46823.0 59380 48.6 0.4428 39383 30.9 19.528602
130 Martin County FL 05000US12085 -80.398211 27.079954 158701 139122.0 9164.0 1252.0 965.0 179.0 5813.0 3725.0 6810.0 30871.0 41857.0 36545.0 17211 51.5 0.5205 54620 34.7 15.237551
131 Miami-Dade County FL 05000US12086 -80.499045 25.610494 2712945 2019915.0 476506.0 43745.0 4683.0 1265.0 124482.0 150429.0 165925.0 542835.0 483546.0 537034.0 488306 39.9 0.5282 45935 38.7 20.921561
132 Monroe County FL 05000US12087 -81.206777 25.601043 79077 NaN NaN NaN NaN NaN NaN 1296.0 3756.0 16448.0 17524.0 20034.0 7548 46.3 0.4610 65717 35.8 12.761396
133 Nassau County FL 05000US12089 -81.764929 30.605926 80622 NaN NaN NaN NaN NaN NaN 585.0 3295.0 17730.0 19147.0 18017.0 8423 45.2 0.4410 71515 30.1 10.521491
134 Okaloosa County FL 05000US12091 -86.594194 30.665858 201170 155446.0 20398.0 5770.0 590.0 327.0 8785.0 2106.0 7904.0 35310.0 48611.0 41852.0 19590 37.2 0.4322 60026 29.4 10.403143
135 Orange County FL 05000US12095 -81.323295 28.514435 1314367 851713.0 276302.0 70844.0 1697.0 50.0 67741.0 34791.0 68209.0 212756.0 252104.0 294893.0 212247 34.9 0.4800 51335 33.3 12.042304
136 Osceola County FL 05000US12097 -81.139312 28.059027 336015 248442.0 40387.0 9588.0 739.0 142.0 23289.0 7840.0 17307.0 71686.0 76165.0 44184.0 49508 35.6 0.4042 51436 33.5 12.771475
137 Palm Beach County FL 05000US12099 -80.448673 26.645763 1443810 1079513.0 266755.0 38391.0 1811.0 536.0 26821.0 50604.0 69011.0 259717.0 292543.0 368824.0 178288 44.7 0.5179 57580 35.1 12.357069
138 Pasco County FL 05000US12101 -82.455707 28.302024 512368 452055.0 28037.0 12829.0 1337.0 190.0 6111.0 9214.0 28228.0 127733.0 116871.0 85638.0 66699 44.8 0.4581 46264 32.3 16.547222
139 Pinellas County FL 05000US12103 -82.739518 27.903122 960730 784811.0 97341.0 33027.0 3117.0 820.0 15140.0 14846.0 46810.0 206961.0 243061.0 215630.0 125474 48.0 0.4835 50036 31.1 14.761533
140 Polk County FL 05000US12105 -81.692783 27.953115 666149 525140.0 100793.0 12565.0 1818.0 306.0 9710.0 21477.0 41221.0 165401.0 130424.0 92755.0 106975 40.2 0.4455 46355 28.8 19.322172
141 Putnam County FL 05000US12107 -81.740894 29.606006 72277 NaN NaN NaN NaN NaN NaN 1448.0 7287.0 19347.0 13284.0 7920.0 14099 45.3 0.4254 38239 30.7 27.272079
142 St. Johns County FL 05000US12109 -81.383914 29.890593 235087 208208.0 12331.0 6363.0 854.0 695.0 1850.0 1898.0 4771.0 35736.0 52134.0 71152.0 17568 43.0 0.4658 78581 30.0 7.829000
143 St. Lucie County FL 05000US12111 -80.443364 27.380775 306507 227646.0 59949.0 6005.0 1345.0 0.0 5409.0 8132.0 18212.0 73041.0 70865.0 45146.0 54771 45.2 0.4439 44804 37.3 14.674288
144 Santa Rosa County FL 05000US12113 -87.014255 30.703633 170497 146607.0 10669.0 3459.0 1460.0 506.0 772.0 1564.0 9139.0 31497.0 43250.0 32987.0 16505 40.3 0.4223 63619 28.4 6.957633
145 Sarasota County FL 05000US12115 -82.365835 27.184385 412569 375600.0 17710.0 6544.0 1005.0 22.0 4215.0 6026.0 14867.0 99454.0 95597.0 110253.0 43026 55.5 0.4744 54989 30.8 12.437071
146 Seminole County FL 05000US12117 -81.131980 28.690079 455479 354325.0 51494.0 19098.0 449.0 343.0 13835.0 3839.0 12523.0 68705.0 114724.0 118505.0 52799 39.2 0.4558 61311 29.9 8.482295
147 Sumter County FL 05000US12119 -82.074715 28.714294 123996 NaN NaN NaN NaN NaN NaN 1748.0 5611.0 35180.0 32938.0 31566.0 11366 67.3 0.4363 54562 34.8 10.832159
148 Volusia County FL 05000US12127 -81.161813 29.057617 529364 441448.0 55747.0 9730.0 2528.0 64.0 7275.0 7300.0 29632.0 122815.0 134522.0 94387.0 72083 46.8 0.4612 45366 33.3 18.833484
149 Walton County FL 05000US12131 -86.176614 30.631211 65889 NaN NaN NaN NaN NaN NaN 595.0 4672.0 12158.0 15793.0 13465.0 7529 44.9 0.4775 56246 31.6 16.833903
150 Barrow County GA 05000US13013 -83.712303 33.992009 77126 NaN NaN NaN NaN NaN NaN 1788.0 5481.0 17219.0 15746.0 9276.0 10941 35.5 0.3880 54256 25.6 12.020913
151 Bartow County GA 05000US13015 -84.838188 34.240918 103807 NaN NaN NaN NaN NaN NaN 2782.0 8067.0 23540.0 20766.0 13554.0 17105 37.2 0.4262 51405 24.8 12.497941
152 Bibb County GA 05000US13021 -83.694193 32.808844 152760 61273.0 82574.0 3089.0 98.0 0.0 1592.0 2127.0 10315.0 29533.0 28267.0 25607.0 38556 36.1 0.5164 36724 34.6 23.550926
153 Bulloch County GA 05000US13031 -81.743810 32.393408 74722 NaN NaN NaN NaN NaN NaN 855.0 4572.0 10331.0 13569.0 11173.0 16546 28.4 0.4579 43982 31.7 18.424141
154 Carroll County GA 05000US13045 -85.080527 33.582237 116261 NaN NaN NaN NaN NaN NaN 2089.0 8412.0 22986.0 21550.0 15453.0 14584 34.1 0.4031 51228 23.9 13.920426
155 Catoosa County GA 05000US13047 -85.137353 34.899393 66398 NaN NaN NaN NaN NaN NaN 149.0 4613.0 15335.0 14712.0 10773.0 6033 40.0 0.4271 55717 29.3 13.199693
156 Chatham County GA 05000US13051 -81.091768 31.974756 289082 151815.0 114139.0 6369.0 650.0 558.0 4208.0 2330.0 15502.0 45036.0 65263.0 61637.0 43398 35.3 0.4887 53964 27.4 11.475246
157 Cherokee County GA 05000US13057 -84.475057 34.244317 241689 NaN NaN NaN NaN NaN NaN 4311.0 9479.0 35214.0 50180.0 61072.0 17610 38.7 0.4147 77950 26.0 6.159024
158 Clarke County GA 05000US13059 -83.367130 33.952234 124707 78919.0 34893.0 5209.0 181.0 33.0 3137.0 1732.0 5335.0 14696.0 17598.0 28091.0 31693 27.5 0.5139 34999 34.0 15.421651
159 Clayton County GA 05000US13063 -84.412977 33.552242 279462 NaN NaN NaN NaN NaN NaN 8668.0 13942.0 55868.0 55567.0 35429.0 56658 32.7 0.4057 45252 29.4 15.258729
160 Cobb County GA 05000US13067 -84.574166 33.939940 748150 439448.0 202244.0 39953.0 3133.0 676.0 36792.0 16578.0 20973.0 90843.0 130974.0 232294.0 70868 36.5 0.4483 70947 27.7 7.929512
161 Columbia County GA 05000US13073 -82.251342 33.550556 147450 108438.0 24938.0 6402.0 932.0 0.0 1413.0 1593.0 3186.0 26000.0 30392.0 34644.0 10627 36.2 0.4068 72737 26.3 16.965883
162 Coweta County GA 05000US13077 -84.762138 33.352896 140526 NaN NaN NaN NaN NaN NaN 831.0 7943.0 28082.0 27749.0 27011.0 13165 39.0 0.4465 71220 26.8 8.781657
163 DeKalb County GA 05000US13089 -84.226343 33.770661 740321 259015.0 403352.0 45634.0 5253.0 101.0 9586.0 18932.0 30805.0 104011.0 124396.0 216819.0 125609 35.4 0.4946 56109 31.5 13.115413
164 Dougherty County GA 05000US13095 -84.214444 31.535068 90017 NaN NaN NaN NaN NaN NaN 1745.0 5711.0 16770.0 19574.0 12183.0 25847 35.2 0.5123 37222 32.3 23.853024
165 Douglas County GA 05000US13097 -84.765944 33.699317 142224 67268.0 67469.0 2242.0 73.0 0.0 2812.0 1699.0 7216.0 28924.0 27918.0 24761.0 17528 36.0 0.4369 62445 26.4 10.666449
166 Fayette County GA 05000US13113 -84.493941 33.412717 111627 NaN NaN NaN NaN NaN NaN 1057.0 1849.0 15439.0 20196.0 37209.0 6161 43.3 0.4401 80626 27.4 7.880937
167 Floyd County GA 05000US13115 -85.213730 34.263677 96560 NaN NaN NaN NaN NaN NaN 2507.0 7003.0 19853.0 17558.0 15426.0 11739 38.7 0.4659 49865 25.7 18.158199
168 Forsyth County GA 05000US13117 -84.127336 34.225143 221009 179397.0 7490.0 25847.0 387.0 0.0 3346.0 3868.0 6205.0 23691.0 35513.0 71739.0 13001 38.7 0.3969 100909 24.9 7.033847
169 Fulton County GA 05000US13121 -84.468182 33.790034 1023336 455532.0 447187.0 68259.0 9001.0 879.0 11948.0 11047.0 38213.0 125877.0 154622.0 352495.0 155061 35.4 0.5369 63510 29.8 10.458777
170 Glynn County GA 05000US13127 -81.496517 31.212747 84502 NaN NaN NaN NaN NaN NaN 1437.0 5374.0 15176.0 18179.0 16060.0 17140 41.0 0.4926 48926 29.0 18.613219
171 Gwinnett County GA 05000US13135 -84.022938 33.959101 907135 438099.0 248723.0 103900.0 1346.0 82.0 88207.0 29301.0 32804.0 132550.0 172483.0 199463.0 102496 35.2 0.4307 67155 31.1 9.450251
172 Hall County GA 05000US13139 -83.818497 34.317588 196637 168678.0 14687.0 3451.0 412.0 99.0 4766.0 11579.0 15316.0 37581.0 33288.0 27404.0 25795 36.5 0.4268 54917 27.3 13.438538
173 Henry County GA 05000US13151 -84.154440 33.452881 221768 110602.0 94657.0 7935.0 0.0 0.0 2709.0 2443.0 11818.0 39676.0 46471.0 41615.0 21047 36.5 0.4165 66905 25.6 7.386570
174 Houston County GA 05000US13153 -83.662856 32.458381 152122 93711.0 45011.0 4623.0 622.0 0.0 1763.0 2178.0 7150.0 22647.0 37813.0 30115.0 24445 35.4 0.4108 62493 27.4 14.493480
175 Liberty County GA 05000US13179 -81.457969 31.807244 62570 29415.0 25722.0 940.0 295.0 12.0 1916.0 706.0 2326.0 10858.0 14763.0 6309.0 7862 27.2 0.3974 45138 29.9 14.216626
176 Lowndes County GA 05000US13185 -83.268967 30.833680 114628 67078.0 44310.0 418.0 256.0 113.0 878.0 1350.0 6378.0 23796.0 18742.0 16336.0 24127 30.5 0.5100 41449 31.0 19.380433
177 Muscogee County GA 05000US13215 -84.874946 32.510197 197485 86603.0 90127.0 4160.0 1159.0 0.0 4517.0 2688.0 11025.0 33963.0 43371.0 35079.0 41236 33.7 0.5000 40060 31.6 19.129102
178 Newton County GA 05000US13217 -83.855189 33.544046 106999 NaN NaN NaN NaN NaN NaN 2497.0 8403.0 20870.0 22271.0 11465.0 23299 36.0 0.4600 48628 32.0 14.629449
179 Paulding County GA 05000US13223 -84.866979 33.920903 155825 NaN NaN NaN NaN NaN NaN 2348.0 6222.0 33021.0 32812.0 23359.0 13462 36.4 0.3834 60856 28.9 7.034874
180 Richmond County GA 05000US13245 -82.074998 33.361487 201647 76547.0 114495.0 4084.0 584.0 848.0 2376.0 3311.0 14721.0 40910.0 40681.0 29069.0 48523 34.0 0.4641 41419 30.1 19.079424
181 Rockdale County GA 05000US13247 -84.026370 33.652081 89355 NaN NaN NaN NaN NaN NaN 1805.0 3902.0 21218.0 16366.0 14381.0 12616 38.4 0.4123 56820 24.7 18.162667
182 Troup County GA 05000US13285 -85.028360 33.034482 70005 NaN NaN NaN NaN NaN NaN 1914.0 7008.0 14267.0 11844.0 9518.0 14790 36.4 0.4594 42371 31.6 25.795125
183 Walker County GA 05000US13295 -85.305385 34.735827 67896 NaN NaN NaN NaN NaN NaN 1562.0 7979.0 15723.0 13766.0 7932.0 13758 41.4 0.4816 39209 33.7 19.932298
184 Walton County GA 05000US13297 -83.734215 33.782649 90184 NaN NaN NaN NaN NaN NaN 1193.0 5914.0 19910.0 16763.0 13280.0 12057 38.3 0.4335 53202 36.3 16.080091
185 Whitfield County GA 05000US13313 -84.968541 34.801726 104589 NaN NaN NaN NaN NaN NaN 6993.0 11171.0 18114.0 18613.0 8233.0 18184 35.1 0.4695 46399 24.3 18.977634
186 Hawaii County HI 05000US15001 -155.502443 19.597764 198449 64714.0 1586.0 47415.0 293.0 26900.0 2422.0 2514.0 6295.0 46005.0 45084.0 38574.0 30154 42.6 0.4674 55750 29.6 17.932338
187 Honolulu County HI 05000US15003 -158.201976 21.461364 992605 209223.0 23269.0 421281.0 1636.0 93165.0 8029.0 21351.0 31333.0 176857.0 214356.0 235112.0 81533 37.5 0.4300 80513 34.7 11.403472
188 Kauai County HI 05000US15007 -159.705965 22.012038 72029 23326.0 621.0 24200.0 563.0 5678.0 183.0 1223.0 1665.0 16790.0 16082.0 14086.0 4333 42.3 0.4198 71344 31.0 13.978634
189 Maui County HI 05000US15009 -156.601550 20.855931 165379 60712.0 422.0 50531.0 157.0 18797.0 1687.0 2121.0 4282.0 38072.0 41393.0 28799.0 13544 41.1 0.4613 72257 28.8 10.503246
190 Black Hawk County IA 05000US19013 -92.306059 42.472888 132904 113334.0 12012.0 3374.0 802.0 719.0 854.0 1499.0 4261.0 26567.0 26296.0 23543.0 21740 35.2 0.4828 50470 28.9 16.700013
191 Dallas County IA 05000US19049 -94.040706 41.685321 84516 76654.0 1003.0 3579.0 102.0 0.0 637.0 1486.0 923.0 9098.0 15841.0 27207.0 3755 35.2 0.4528 75899 31.3 8.875829
192 Dubuque County IA 05000US19061 -90.878771 42.463481 97003 NaN NaN NaN NaN NaN NaN 410.0 3248.0 21481.0 19938.0 18922.0 9908 38.0 0.4751 60456 24.8 13.041634
193 Johnson County IA 05000US19103 -91.588812 41.668737 146547 119612.0 10314.0 10102.0 0.0 0.0 3928.0 1394.0 2670.0 13640.0 20927.0 46917.0 25733 30.0 0.4813 58064 38.3 8.242306
194 Linn County IA 05000US19113 -91.597673 42.077951 221661 198263.0 12247.0 5323.0 239.0 132.0 529.0 1417.0 5968.0 39482.0 48421.0 52954.0 22115 37.6 0.4330 64639 25.8 13.054400
195 Polk County IA 05000US19153 -93.569720 41.684281 474045 396575.0 31968.0 22224.0 1315.0 150.0 8740.0 9183.0 15514.0 74309.0 98010.0 112049.0 50855 35.3 0.4372 64067 28.0 11.136920
196 Pottawattamie County IA 05000US19155 -95.544905 41.340184 93582 NaN NaN NaN NaN NaN NaN 298.0 4339.0 21030.0 22793.0 13517.0 7916 39.1 0.4568 55972 27.7 16.171663
197 Scott County IA 05000US19163 -90.622290 41.641679 172474 147577.0 14802.0 4441.0 554.0 0.0 2285.0 1089.0 5316.0 36898.0 35324.0 36493.0 22805 37.9 0.4585 54730 32.9 20.220362
198 Story County IA 05000US19169 -93.466093 42.037538 97090 84190.0 1423.0 8417.0 178.0 0.0 340.0 119.0 1052.0 9545.0 12491.0 28328.0 18802 26.9 0.4647 53371 38.9 7.199465
199 Woodbury County IA 05000US19193 -96.053296 42.393220 102779 87253.0 3720.0 3154.0 723.0 749.0 3083.0 2480.0 5867.0 19764.0 20874.0 14802.0 12551 35.1 0.4411 52324 27.7 17.190776
200 Ada County ID 05000US16001 -116.244456 43.447861 444028 402851.0 6535.0 11778.0 2026.0 377.0 6847.0 2911.0 10492.0 66998.0 102929.0 113442.0 47122 36.6 0.4696 61301 28.0 21.753499
201 Bannock County ID 05000US16005 -112.228986 42.692939 84377 74610.0 280.0 1480.0 3335.0 0.0 2279.0 1296.0 3098.0 15031.0 18229.0 14713.0 13683 34.0 0.4654 48429 27.1 13.010696
202 Bonneville County ID 05000US16019 -111.621878 43.395171 112232 99184.0 0.0 1521.0 843.0 0.0 7841.0 576.0 4067.0 16183.0 25007.0 21998.0 11958 33.0 0.4208 59706 25.2 8.475458
203 Canyon County ID 05000US16027 -116.708527 43.623051 211698 171868.0 48.0 2978.0 2689.0 835.0 27575.0 5518.0 12731.0 41785.0 41778.0 24757.0 31822 32.7 0.4086 48437 28.3 14.555032
204 Kootenai County ID 05000US16055 -116.694918 47.677113 154311 143862.0 1029.0 1352.0 2970.0 167.0 1836.0 685.0 6502.0 28908.0 41719.0 27414.0 24813 39.6 0.4217 51765 30.7 13.515074
205 Twin Falls County ID 05000US16083 -114.665639 42.352309 83514 NaN NaN NaN NaN NaN NaN 1407.0 3657.0 14238.0 21566.0 11629.0 12017 34.7 0.4229 51210 34.2 11.663734
206 Adams County IL 05000US17001 -91.194961 39.986052 66578 NaN NaN NaN NaN NaN NaN 392.0 3048.0 16117.0 14706.0 11787.0 8041 42.1 0.4600 51624 31.0 19.289414
207 Champaign County IL 05000US17019 -88.197201 40.139150 208419 151060.0 27686.0 21908.0 331.0 62.0 1244.0 712.0 4064.0 25018.0 34726.0 55187.0 37842 29.9 0.5131 50335 32.7 10.757916
208 Cook County IL 05000US17031 -87.645455 41.894294 5203499 2919459.0 1223763.0 371491.0 14508.0 1939.0 537416.0 173260.0 234099.0 832486.0 909459.0 1344758.0 763242 36.5 0.5049 60046 29.7 15.545224
209 DeKalb County IL 05000US17037 -88.768991 41.894613 104528 86721.0 7396.0 2439.0 117.0 0.0 4515.0 786.0 3740.0 15552.0 20750.0 19341.0 18120 30.9 0.4382 59285 30.3 9.604797
210 DuPage County IL 05000US17043 -88.086038 41.852058 929368 714083.0 45918.0 107270.0 1060.0 481.0 35093.0 16291.0 23995.0 117199.0 161110.0 307628.0 63806 39.0 0.4554 84908 29.2 7.904606
211 Kane County IL 05000US17089 -88.428039 41.939594 531715 374272.0 29929.0 21320.0 2405.0 33.0 91408.0 23571.0 22472.0 75833.0 100321.0 115853.0 56729 37.2 0.4488 73347 30.5 10.951186
212 Kankakee County IL 05000US17091 -87.861125 41.139494 110008 86918.0 17306.0 1023.0 394.0 0.0 2799.0 2030.0 6096.0 24885.0 24039.0 13842.0 14338 38.2 0.4113 54911 24.4 17.604416
213 Kendall County IL 05000US17093 -88.430626 41.588140 124695 NaN NaN NaN NaN NaN NaN 2335.0 4293.0 16998.0 26126.0 27471.0 5968 35.2 0.3544 90482 31.6 6.863467
214 Lake County IL 05000US17097 -87.436118 42.326443 703047 530524.0 46836.0 52572.0 1206.0 310.0 52919.0 16075.0 21154.0 93189.0 115963.0 202633.0 58653 38.5 0.4909 83152 30.0 9.020418
215 LaSalle County IL 05000US17099 -88.885931 41.343341 110642 NaN NaN NaN NaN NaN NaN 1158.0 6319.0 27210.0 26599.0 14744.0 14332 42.6 0.4108 57476 27.2 17.107233
216 McHenry County IL 05000US17111 -88.452245 42.324298 307004 284322.0 3937.0 8478.0 253.0 0.0 3878.0 4041.0 8777.0 54641.0 66576.0 69513.0 23974 39.7 0.4013 81063 29.6 7.037763
217 McLean County IL 05000US17113 -88.844539 40.494559 172418 142803.0 14364.0 8635.0 380.0 0.0 1723.0 891.0 2111.0 26611.0 29498.0 46477.0 22022 32.9 0.4466 62156 25.5 11.822863
218 Macon County IL 05000US17115 -88.961529 39.860237 106550 83242.0 13359.0 1089.0 262.0 13.0 529.0 1202.0 5192.0 24793.0 24649.0 15750.0 17702 41.6 0.4613 46198 31.2 16.869688
219 Madison County IL 05000US17119 -89.900195 38.827082 265759 233639.0 23424.0 3169.0 299.0 0.0 1132.0 986.0 11223.0 53485.0 69435.0 49032.0 34795 40.4 0.4343 56035 29.7 13.760963
220 Peoria County IL 05000US17143 -89.767358 40.785999 185006 135178.0 33901.0 7607.0 618.0 0.0 2033.0 1643.0 9115.0 32742.0 41126.0 37941.0 26789 37.3 0.4770 51975 29.2 19.512628
221 Rock Island County IL 05000US17161 -90.572203 41.468404 144784 116660.0 13890.0 3486.0 778.0 283.0 2819.0 2317.0 7722.0 28385.0 37898.0 22961.0 22647 39.0 0.4210 50948 24.2 15.355612
222 St. Clair County IL 05000US17163 -89.928546 38.470198 262759 168589.0 77768.0 4473.0 712.0 78.0 4008.0 2107.0 9961.0 52707.0 60906.0 49870.0 37795 38.7 0.4857 50267 33.0 19.392644
223 Sangamon County IL 05000US17167 -89.662311 39.756378 197499 162397.0 25993.0 3568.0 143.0 124.0 719.0 1203.0 7309.0 35861.0 41024.0 48467.0 27436 40.0 0.4618 53782 29.3 14.766330
224 Tazewell County IL 05000US17179 -89.516260 40.508074 134385 NaN NaN NaN NaN NaN NaN 1283.0 5157.0 29458.0 34581.0 22478.0 10643 41.2 0.4104 60152 24.9 15.159408
225 Vermilion County IL 05000US17183 -87.726771 40.186740 78111 NaN NaN NaN NaN NaN NaN 1123.0 4168.0 20700.0 18472.0 7408.0 15680 40.5 0.4462 45481 33.8 20.655851
226 Will County IL 05000US17197 -87.978456 41.448474 689529 505732.0 76425.0 38308.0 2016.0 515.0 47269.0 12094.0 21805.0 118831.0 134033.0 155328.0 47321 37.9 0.4118 81438 30.5 9.688777
227 Williamson County IL 05000US17199 -88.930018 37.730353 67560 NaN NaN NaN NaN NaN NaN 199.0 3021.0 13083.0 18780.0 11670.0 10078 41.3 0.4294 48409 26.0 23.433355
228 Winnebago County IL 05000US17201 -89.161205 42.337396 285873 226518.0 35905.0 7285.0 217.0 140.0 5520.0 4577.0 19707.0 60547.0 64214.0 43587.0 43941 39.8 0.4558 49749 27.6 15.510974
229 Allen County IN 05000US18003 -85.072230 41.091855 370404 296773.0 42515.0 14669.0 461.0 102.0 4098.0 6248.0 15687.0 68073.0 77004.0 69172.0 54437 35.8 0.4479 51173 27.0 13.752259
230 Bartholomew County IN 05000US18005 -85.897999 39.205843 81402 NaN NaN NaN NaN NaN NaN 1810.0 5115.0 15917.0 13351.0 18111.0 10961 37.5 0.4381 59102 24.5 15.806615
231 Clark County IN 05000US18019 -85.711122 38.476217 116031 102109.0 6608.0 457.0 79.0 0.0 1181.0 1041.0 6483.0 26941.0 27514.0 16255.0 11165 38.7 0.4195 51401 26.8 17.292578
232 Delaware County IN 05000US18035 -85.398856 40.227165 115603 101294.0 8952.0 1473.0 226.0 340.0 979.0 885.0 5322.0 25957.0 21608.0 16007.0 23511 35.4 0.4806 41041 30.5 17.735445
233 Elkhart County IN 05000US18039 -85.863986 41.600693 203781 180922.0 11251.0 2149.0 1040.0 360.0 3346.0 6052.0 16482.0 46946.0 31147.0 24192.0 28611 36.2 0.4073 54216 25.4 20.024280
234 Floyd County IN 05000US18043 -85.911474 38.317937 76990 69860.0 3954.0 800.0 183.0 0.0 252.0 1160.0 3861.0 16432.0 16055.0 14803.0 6640 40.4 0.4805 58586 29.5 19.113668
235 Grant County IN 05000US18053 -85.654945 40.515757 66937 NaN NaN NaN NaN NaN NaN 1001.0 4529.0 17307.0 13197.0 7339.0 13141 39.9 0.4207 37117 30.9 22.726930
236 Hamilton County IN 05000US18057 -86.020586 40.049870 316373 276282.0 11797.0 17484.0 322.0 0.0 2438.0 935.0 6645.0 31917.0 46937.0 118839.0 17542 36.9 0.4352 89823 24.5 5.021895
237 Hancock County IN 05000US18059 -85.772904 39.822604 73717 NaN NaN NaN NaN NaN NaN 439.0 2567.0 16243.0 15529.0 15236.0 7287 39.9 0.4027 67799 27.8 16.876430
238 Hendricks County IN 05000US18063 -86.510286 39.768749 160610 NaN NaN NaN NaN NaN NaN 419.0 6873.0 30447.0 28109.0 40072.0 9431 36.8 0.3758 78307 28.5 9.787481
239 Howard County IN 05000US18067 -86.114118 40.483537 82568 NaN NaN NaN NaN NaN NaN 617.0 4233.0 22394.0 17515.0 12186.0 12250 41.4 0.4556 45702 26.9 15.478871
240 Johnson County IN 05000US18081 -86.094600 39.495986 151982 NaN NaN NaN NaN NaN NaN 1128.0 7171.0 30714.0 27227.0 32410.0 9860 37.5 0.4376 65991 27.4 13.320010
241 Kosciusko County IN 05000US18085 -85.861575 41.244293 79092 75552.0 958.0 407.0 465.0 0.0 670.0 1394.0 6180.0 18586.0 13367.0 11059.0 8715 38.7 0.4303 53963 29.2 19.856365
242 Lake County IN 05000US18089 -87.374337 41.472239 485846 299056.0 117223.0 6144.0 1531.0 95.0 48308.0 8342.0 24837.0 114124.0 103624.0 72277.0 77634 38.6 0.4582 53681 28.3 17.685033
243 LaPorte County IN 05000US18091 -86.744729 41.549011 110015 91255.0 11928.0 889.0 610.0 63.0 2249.0 1052.0 5815.0 30323.0 24621.0 13954.0 16505 40.8 0.4309 53507 25.6 17.317108
244 Madison County IN 05000US18095 -85.722454 40.166203 129296 113364.0 9062.0 554.0 431.0 0.0 1179.0 1419.0 8494.0 36318.0 27035.0 15260.0 22098 40.4 0.4429 45495 28.2 17.555529
245 Marion County IN 05000US18097 -86.135794 39.782976 941229 577828.0 260219.0 27885.0 2924.0 295.0 39113.0 18287.0 50420.0 173858.0 178149.0 186033.0 171692 34.3 0.4952 44874 30.6 18.093439
246 Monroe County IN 05000US18105 -86.523325 39.160751 145496 125427.0 4068.0 9461.0 310.0 0.0 848.0 342.0 5763.0 19840.0 19297.0 35619.0 33297 28.5 0.5152 43582 36.0 11.197329
247 Morgan County IN 05000US18109 -86.447457 39.482646 69698 NaN NaN NaN NaN NaN NaN 400.0 3752.0 20735.0 14987.0 7740.0 7095 42.3 0.3740 60530 25.7 13.550481
248 Porter County IN 05000US18127 -87.071308 41.509922 167791 153318.0 5280.0 2055.0 224.0 129.0 1705.0 1913.0 6650.0 37808.0 37217.0 29471.0 11284 39.1 0.4196 66196 27.6 14.641349
249 St. Joseph County IN 05000US18141 -86.288159 41.617699 269141 209203.0 35405.0 6408.0 1880.0 190.0 9023.0 3351.0 14766.0 55910.0 47525.0 49076.0 41875 36.5 0.4688 48358 27.9 21.740788
250 Tippecanoe County IN 05000US18157 -86.893943 40.389260 188059 152857.0 9666.0 15385.0 268.0 200.0 6020.0 2045.0 5789.0 27194.0 29256.0 38232.0 32247 28.2 0.4730 51361 34.4 11.892406
251 Vanderburgh County IN 05000US18163 -87.586166 38.020070 181721 155227.0 15804.0 1990.0 310.0 1210.0 1808.0 1015.0 12387.0 37585.0 39122.0 31718.0 31154 37.7 0.4625 46064 29.6 19.747092
252 Vigo County IN 05000US18167 -87.390375 39.429143 107931 94021.0 6808.0 2044.0 427.0 47.0 686.0 1227.0 5676.0 21572.0 21886.0 17737.0 16294 35.7 0.4289 43910 36.7 14.719302
253 Wayne County IN 05000US18177 -85.006735 39.863091 66568 NaN NaN NaN NaN NaN NaN 771.0 3204.0 19100.0 13186.0 7987.0 10426 40.4 0.4303 43401 26.3 24.952898
254 Butler County KS 05000US20015 -96.838762 37.773681 67025 NaN NaN NaN NaN NaN NaN 73.0 3201.0 11978.0 15557.0 12768.0 4857 38.4 0.4117 60182 25.5 14.922113
255 Douglas County KS 05000US20045 -95.290529 38.896573 119440 97894.0 4866.0 6249.0 1800.0 26.0 2960.0 600.0 2559.0 12743.0 16972.0 35978.0 19125 30.0 0.4659 56345 33.2 10.804880
256 Johnson County KS 05000US20091 -94.822330 38.883907 584451 505785.0 25393.0 27644.0 1260.0 332.0 5627.0 4901.0 10117.0 55924.0 102829.0 217267.0 32437 37.6 0.4457 80553 27.2 5.911190
257 Leavenworth County KS 05000US20103 -95.038977 39.189511 80204 64838.0 7435.0 1697.0 463.0 295.0 3120.0 208.0 2204.0 15327.0 17439.0 18116.0 5777 36.7 0.4202 68299 24.5 12.037241
258 Riley County KS 05000US20161 -96.727489 39.291211 73343 60227.0 4370.0 3820.0 182.0 264.0 998.0 NaN NaN NaN NaN NaN 12529 25.2 0.4603 50737 29.9 7.922266
259 Sedgwick County KS 05000US20173 -97.459451 37.683807 511995 400297.0 42474.0 21236.0 4630.0 0.0 18824.0 7863.0 23613.0 85423.0 104750.0 103878.0 75984 35.1 0.4489 52193 27.6 15.302199
260 Shawnee County KS 05000US20177 -95.755664 39.041805 178146 143411.0 14949.0 2374.0 1773.0 127.0 9288.0 2154.0 6369.0 39983.0 35016.0 35972.0 17150 38.4 0.4436 55710 27.0 21.939773
261 Wyandotte County KS 05000US20209 -94.763087 39.115384 163831 96425.0 37308.0 6063.0 1335.0 0.0 15480.0 7094.0 13040.0 33589.0 28248.0 18506.0 30865 33.9 0.4176 43129 28.2 23.140934
262 Boone County KY 05000US21015 -84.731444 38.974595 128536 NaN NaN NaN NaN NaN NaN 726.0 3699.0 20816.0 31063.0 25263.0 9664 37.0 0.4280 72374 27.6 8.593567
263 Bullitt County KY 05000US21029 -85.703036 37.969572 79151 NaN NaN NaN NaN NaN NaN 721.0 5831.0 21501.0 16811.0 8304.0 8372 39.9 0.4168 65359 27.6 14.381651
264 Campbell County KY 05000US21037 -84.379583 38.946981 92211 NaN NaN NaN NaN NaN NaN 1069.0 2797.0 18819.0 16450.0 22547.0 11420 37.7 0.4355 62536 28.0 14.319410
265 Christian County KY 05000US21047 -87.493554 36.893388 72351 51567.0 16784.0 420.0 251.0 433.0 1227.0 452.0 3709.0 13415.0 16118.0 6308.0 14409 28.5 0.4613 41140 29.4 24.720353
266 Daviess County KY 05000US21059 -87.087139 37.731671 99674 NaN NaN NaN NaN NaN NaN 1175.0 4197.0 20840.0 23587.0 15602.0 16694 38.9 0.4748 51764 27.8 16.195211
267 Fayette County KY 05000US21067 -84.458443 38.040157 318449 238917.0 46097.0 11699.0 1080.0 201.0 10307.0 5269.0 11751.0 41326.0 54655.0 90182.0 54232 34.4 0.4942 53178 28.9 11.121861
268 Hardin County KY 05000US21093 -85.963183 37.695836 107316 84280.0 13475.0 1851.0 175.0 275.0 2024.0 1037.0 4474.0 20651.0 26804.0 16654.0 14717 37.3 0.4394 52148 25.2 14.461748
269 Jefferson County KY 05000US21111 -85.657624 38.189533 765352 551075.0 165658.0 21038.0 1477.0 409.0 5410.0 8480.0 37311.0 137233.0 169097.0 168689.0 106861 38.3 0.4767 51991 27.8 13.278542
270 Kenton County KY 05000US21117 -84.533492 38.930477 164945 149375.0 7334.0 1922.0 241.0 0.0 1667.0 2509.0 8243.0 30245.0 34821.0 34126.0 20482 37.4 0.4235 62182 25.6 10.556682
271 McCracken County KY 05000US21145 -88.712378 37.053688 65162 NaN NaN NaN NaN NaN NaN 684.0 3644.0 12752.0 18176.0 9231.0 12791 40.7 0.5034 39048 29.0 22.248029
272 Madison County KY 05000US21151 -84.277008 37.723528 89547 NaN NaN NaN NaN NaN NaN 1245.0 4483.0 15985.0 15785.0 15612.0 18998 34.0 0.4557 43840 34.5 18.862427
273 Oldham County KY 05000US21185 -85.456059 38.400046 65560 60007.0 1848.0 834.0 361.0 0.0 523.0 241.0 1666.0 8908.0 14267.0 17663.0 3917 38.4 0.4344 90341 30.7 5.717658
274 Warren County KY 05000US21227 -86.423579 36.995634 125532 103029.0 11775.0 4718.0 642.0 0.0 2655.0 2058.0 5526.0 20225.0 22392.0 24066.0 22748 33.1 0.4872 46686 28.1 11.864514
275 Ascension Parish LA 05000US22005 -90.910023 30.202946 121587 NaN NaN NaN NaN NaN NaN 1698.0 6975.0 25133.0 23758.0 19153.0 11764 35.1 0.4165 76581 26.3 8.358974
276 Bossier Parish LA 05000US22015 -93.617977 32.696202 126057 91878.0 28446.0 1906.0 275.0 220.0 773.0 1939.0 6925.0 23886.0 27462.0 21579.0 23032 34.7 0.4485 48163 32.6 18.527962
277 Caddo Parish LA 05000US22017 -93.882423 32.577195 248851 114096.0 123912.0 3212.0 552.0 0.0 3470.0 2731.0 16516.0 57001.0 49289.0 37049.0 65090 36.9 0.5444 37104 36.8 27.304935
278 Calcasieu Parish LA 05000US22019 -93.358015 30.229559 200601 140157.0 50582.0 2284.0 747.0 0.0 2541.0 3604.0 13673.0 44163.0 41870.0 26954.0 42143 36.2 0.4777 45962 29.0 25.956458
279 East Baton Rouge Parish LA 05000US22033 -91.093174 30.544002 447037 210180.0 205707.0 14660.0 419.0 0.0 8592.0 4611.0 17875.0 78360.0 81858.0 97181.0 86232 33.3 0.5162 50508 30.1 14.590508
280 Iberia Parish LA 05000US22045 -91.842706 29.606013 73273 NaN NaN NaN NaN NaN NaN 2267.0 6003.0 19233.0 10114.0 6924.0 16982 36.8 0.4309 41424 33.1 23.612805
281 Jefferson Parish LA 05000US22051 -90.036231 29.503300 436523 271283.0 117081.0 18643.0 1899.0 0.0 18732.0 12571.0 28356.0 97084.0 83620.0 78274.0 67693 39.0 0.4849 49678 33.2 22.190264
282 Lafayette Parish LA 05000US22055 -92.064170 30.206506 241398 168941.0 61221.0 4038.0 401.0 0.0 1594.0 3340.0 12377.0 48517.0 43174.0 51322.0 45278 34.1 0.4956 49969 29.0 12.758892
283 Lafourche Parish LA 05000US22057 -90.394849 29.491992 98305 77516.0 13471.0 504.0 2473.0 73.0 2617.0 3200.0 9036.0 26143.0 13820.0 11675.0 17167 38.0 0.4359 51772 28.2 22.113498
284 Livingston Parish LA 05000US22063 -90.727474 30.440419 140138 NaN NaN NaN NaN NaN NaN 2436.0 9736.0 37043.0 24425.0 15615.0 18467 36.3 0.4305 56534 23.1 14.903431
285 Orleans Parish LA 05000US22071 -89.939007 30.068636 391495 133434.0 233836.0 11721.0 434.0 97.0 4750.0 7877.0 28169.0 59915.0 70401.0 105791.0 88916 35.7 0.5690 38681 35.5 23.004114
286 Ouachita Parish LA 05000US22073 -92.154798 32.477495 156983 NaN NaN NaN NaN NaN NaN 1498.0 12583.0 38011.0 26777.0 21265.0 37704 35.6 0.5058 37275 34.0 27.426448
287 Rapides Parish LA 05000US22079 -92.535953 31.193204 132424 84072.0 42337.0 2230.0 701.0 0.0 419.0 2645.0 7981.0 32382.0 24186.0 18526.0 25096 37.4 0.4997 42582 33.1 21.566656
288 St. Landry Parish LA 05000US22097 -91.989274 30.583441 83883 NaN NaN NaN NaN NaN NaN 2591.0 7331.0 23070.0 9909.0 7712.0 22384 36.5 0.4942 31207 35.2 33.416936
289 St. Tammany Parish LA 05000US22103 -89.951962 30.410022 253602 210347.0 27933.0 3793.0 1660.0 274.0 3516.0 2130.0 13037.0 46570.0 55250.0 53821.0 21281 39.7 0.4543 64639 30.0 12.131663
290 Tangipahoa Parish LA 05000US22105 -90.406633 30.621581 130710 87647.0 37463.0 730.0 481.0 0.0 577.0 1279.0 13264.0 27623.0 22153.0 16816.0 25960 35.9 0.4788 48162 37.7 16.793701
291 Terrebonne Parish LA 05000US22109 -90.844190 29.333266 113220 79883.0 22901.0 571.0 6376.0 0.0 1450.0 3274.0 7880.0 29616.0 18061.0 10872.0 26021 35.5 0.4809 46026 33.0 22.896614
292 Barnstable County MA 05000US25001 -70.211083 41.798819 214276 196365.0 5175.0 2951.0 914.0 1034.0 2794.0 1053.0 4563.0 42874.0 43873.0 71991.0 13717 53.1 0.4678 67898 33.6 10.608974
293 Berkshire County MA 05000US25003 -73.213948 42.375314 126903 116668.0 3428.0 2049.0 260.0 67.0 1200.0 1198.0 5759.0 28442.0 24614.0 31121.0 11869 46.7 0.4463 58418 27.4 13.987898
294 Bristol County MA 05000US25005 -71.087062 41.748576 558324 469087.0 23337.0 13414.0 105.0 305.0 38545.0 20451.0 25365.0 112509.0 107439.0 113980.0 57480 40.8 0.4580 66027 29.0 15.010363
295 Essex County MA 05000US25009 -70.865107 42.642711 779018 635443.0 30824.0 26984.0 1362.0 366.0 63750.0 20480.0 28643.0 133541.0 137304.0 205985.0 82528 41.3 0.4801 73901 31.5 12.424047
296 Franklin County MA 05000US25011 -72.591655 42.583791 70382 65664.0 692.0 710.0 262.0 293.0 583.0 264.0 2414.0 15551.0 15071.0 19419.0 6051 45.9 0.4626 57106 30.2 16.713101
297 Hampden County MA 05000US25013 -72.635648 42.136197 468467 388013.0 40874.0 10440.0 1009.0 0.0 14956.0 12049.0 25832.0 93476.0 93327.0 83072.0 75434 38.9 0.4518 51544 31.6 18.102372
298 Hampshire County MA 05000US25015 -72.663694 42.339459 161816 143231.0 3958.0 7242.0 72.0 265.0 1101.0 1223.0 3526.0 24640.0 23802.0 45885.0 17078 36.6 0.4638 64354 34.3 9.649801
299 Middlesex County MA 05000US25017 -71.396507 42.479477 1589774 1220690.0 86605.0 187448.0 2454.0 517.0 52376.0 29494.0 35692.0 215733.0 205626.0 611606.0 118943 38.5 0.4566 95249 28.4 9.619050
300 Norfolk County MA 05000US25021 -71.179875 42.169702 697181 546568.0 45901.0 74523.0 843.0 215.0 12800.0 6857.0 18294.0 99200.0 101633.0 256115.0 40963 41.0 0.4840 92696 29.7 8.249523
301 Plymouth County MA 05000US25023 -70.741942 41.987196 513565 430007.0 45757.0 6215.0 688.0 152.0 20248.0 6604.0 17093.0 105106.0 95007.0 128552.0 39173 42.4 0.4362 82087 29.2 10.403788
302 Suffolk County MA 05000US25025 -71.020173 42.331960 784230 432065.0 179093.0 68847.0 2715.0 252.0 42262.0 30838.0 35051.0 123792.0 96388.0 242704.0 146698 32.7 0.5330 61796 30.6 13.076046
303 Worcester County MA 05000US25027 -71.940282 42.311693 819589 683124.0 39855.0 39751.0 1532.0 292.0 33956.0 11440.0 37402.0 157340.0 147222.0 201190.0 75347 40.1 0.4512 69295 29.3 12.897554
304 Allegany County MD 05000US24001 -78.703108 39.612309 72130 63802.0 6374.0 619.0 150.0 27.0 64.0 443.0 3333.0 20514.0 16166.0 9040.0 10294 40.8 0.4265 45606 29.8 20.175312
305 Anne Arundel County MD 05000US24003 -76.560511 38.993374 568346 416095.0 91607.0 20626.0 1034.0 53.0 13384.0 3907.0 20570.0 93197.0 106153.0 165472.0 38160 38.0 0.4147 96483 28.6 6.972980
306 Baltimore County MD 05000US24005 -76.616569 39.443167 831026 512637.0 238123.0 49490.0 2431.0 579.0 6408.0 10551.0 30920.0 154668.0 155005.0 219510.0 72939 39.4 0.4352 72764 28.5 10.739132
307 Calvert County MD 05000US24009 -76.525864 38.521358 91251 73623.0 11839.0 1709.0 0.0 0.0 1064.0 448.0 2863.0 19788.0 20191.0 18826.0 4395 40.6 0.3644 98732 27.6 8.660665
308 Carroll County MD 05000US24013 -77.015512 39.563189 167656 153931.0 5631.0 3078.0 407.0 104.0 555.0 963.0 7277.0 35491.0 31823.0 39753.0 7331 43.2 0.3904 90343 28.6 12.511961
309 Cecil County MD 05000US24015 -75.941584 39.562352 102603 NaN NaN NaN NaN NaN NaN 843.0 6292.0 25122.0 20505.0 16887.0 9877 40.0 0.4041 74221 31.6 10.649936
310 Charles County MD 05000US24017 -77.015427 38.472853 157705 71927.0 71324.0 4466.0 246.0 0.0 1100.0 1571.0 4076.0 36816.0 32951.0 28837.0 10563 38.1 0.3904 95735 31.0 12.852009
311 Frederick County MD 05000US24021 -77.397627 39.470427 247591 200808.0 25712.0 11679.0 1172.0 58.0 2201.0 4224.0 8354.0 41846.0 43749.0 68585.0 17448 39.3 0.4057 90043 29.3 9.528953
312 Harford County MD 05000US24025 -76.299789 39.537429 251032 198064.0 33432.0 5848.0 476.0 78.0 2935.0 3854.0 8781.0 46114.0 50534.0 62906.0 17946 41.2 0.4133 84175 30.5 10.479997
313 Howard County MD 05000US24027 -76.924406 39.252264 317233 186168.0 58167.0 57006.0 405.0 56.0 2907.0 3348.0 4605.0 30124.0 41966.0 131537.0 14639 38.6 0.4010 120941 27.1 4.451671
314 Montgomery County MD 05000US24031 -77.203063 39.137382 1043863 574964.0 191046.0 154518.0 2053.0 239.0 77581.0 28988.0 23024.0 102471.0 131934.0 423556.0 69755 39.0 0.4613 99763 30.0 6.842179
315 Prince George's County MD 05000US24033 -76.847272 38.825880 908049 159377.0 571454.0 38290.0 3802.0 287.0 106267.0 38652.0 39999.0 159212.0 171427.0 195356.0 81035 36.7 0.3894 79184 29.7 10.247356
316 St. Mary's County MD 05000US24037 -76.534270 38.222666 112587 NaN NaN NaN NaN NaN NaN 1173.0 6912.0 22272.0 22592.0 19369.0 10839 35.9 0.3917 78195 30.0 15.997873
317 Washington County MD 05000US24043 -77.814671 39.603621 150292 126158.0 16125.0 2009.0 228.0 253.0 313.0 2441.0 10760.0 38948.0 29853.0 21353.0 19764 40.5 0.4461 54250 27.9 20.007524
318 Wicomico County MD 05000US24045 -75.632206 38.367389 102577 69068.0 26015.0 3530.0 30.0 108.0 933.0 1510.0 4594.0 23577.0 17959.0 16126.0 19639 35.8 0.4305 50844 35.4 17.767988
319 Baltimore city MD 05000US24510 -76.610516 39.300213 614664 189260.0 384465.0 15149.0 1800.0 604.0 9016.0 13283.0 41757.0 125266.0 104018.0 130304.0 130053 34.9 0.5211 47350 29.6 20.636233
320 Androscoggin County ME 05000US23001 -70.207435 44.167681 107319 NaN NaN NaN NaN NaN NaN 820.0 4522.0 28522.0 22964.0 16219.0 12573 40.4 0.4154 49081 28.5 14.317519
321 Aroostook County ME 05000US23003 -68.649410 46.727057 67959 64534.0 904.0 292.0 850.0 13.0 84.0 1184.0 3353.0 17626.0 16063.0 9962.0 11101 47.5 0.4534 39450 28.5 24.667442
322 Cumberland County ME 05000US23005 -70.330375 43.808348 292041 268659.0 8502.0 6084.0 996.0 0.0 661.0 1586.0 6982.0 49944.0 53968.0 95726.0 30276 42.2 0.4572 65913 30.1 9.361367
323 Kennebec County ME 05000US23011 -69.765764 44.417012 120569 115205.0 1336.0 1313.0 607.0 183.0 218.0 1483.0 4829.0 29362.0 26068.0 22904.0 18447 43.3 0.4355 51573 27.3 15.101170
324 Penobscot County ME 05000US23019 -68.657487 45.390602 151806 143639.0 811.0 1404.0 1940.0 0.0 338.0 492.0 4821.0 38783.0 34573.0 26852.0 22236 42.0 0.4529 47328 29.8 13.610765
325 York County ME 05000US23031 -70.670402 43.427239 202343 NaN NaN NaN NaN NaN NaN 1871.0 6912.0 46320.0 46387.0 45129.0 14355 44.9 0.4357 60863 27.2 13.550546
326 Allegan County MI 05000US26005 -86.634745 42.595788 115548 108703.0 1445.0 417.0 307.0 0.0 1013.0 963.0 3727.0 29442.0 24752.0 17114.0 8777 39.4 0.3837 57846 25.6 19.230861
327 Bay County MI 05000US26017 -83.978701 43.699711 104747 NaN NaN NaN NaN NaN NaN 948.0 5252.0 26412.0 28257.0 13348.0 16403 43.1 0.4429 44756 29.7 16.703893
328 Berrien County MI 05000US26021 -86.741822 41.792639 154010 120388.0 22043.0 2977.0 413.0 16.0 2443.0 2060.0 7921.0 29052.0 36455.0 30250.0 24806 41.6 0.4783 47083 29.4 18.521539
329 Calhoun County MI 05000US26025 -85.012385 42.242990 134386 110181.0 14071.0 2622.0 539.0 0.0 848.0 1744.0 5827.0 34381.0 29390.0 18352.0 22036 39.6 0.4545 45902 31.9 20.454545
330 Clinton County MI 05000US26037 -84.591695 42.950455 77888 NaN NaN NaN NaN NaN NaN 579.0 1858.0 14857.0 18196.0 17398.0 7476 40.1 0.4254 65730 25.0 11.539525
331 Eaton County MI 05000US26045 -84.846524 42.589614 109160 95114.0 6008.0 2049.0 572.0 178.0 1071.0 810.0 3794.0 21045.0 30020.0 19445.0 13573 40.9 0.4152 56211 26.8 15.134554
332 Genesee County MI 05000US26049 -83.706372 43.021077 408615 304971.0 81377.0 4111.0 1737.0 305.0 2400.0 3055.0 20269.0 90709.0 106050.0 55990.0 81834 39.9 0.4738 43955 33.6 20.941431
333 Grand Traverse County MI 05000US26055 -85.553848 44.718688 92084 NaN NaN NaN NaN NaN NaN 560.0 2572.0 16555.0 24578.0 21783.0 9739 43.4 0.4709 58532 29.1 14.111994
334 Ingham County MI 05000US26065 -84.373811 42.603534 288051 213921.0 33628.0 19021.0 993.0 187.0 5347.0 2516.0 9338.0 38545.0 54666.0 66044.0 54799 31.8 0.4651 49139 31.3 14.744272
335 Isabella County MI 05000US26073 -84.839424 43.645233 71282 62681.0 1517.0 1392.0 1337.0 0.0 424.0 516.0 1784.0 11139.0 12398.0 11608.0 15601 27.8 0.4938 41868 40.3 13.612040
336 Jackson County MI 05000US26075 -84.420868 42.248474 158460 138283.0 13482.0 1260.0 676.0 116.0 810.0 1324.0 7933.0 31409.0 44255.0 24079.0 19592 41.2 0.4421 50009 29.5 17.147000
337 Kalamazoo County MI 05000US26077 -85.532854 42.246266 261654 212319.0 28131.0 6828.0 373.0 0.0 2359.0 2213.0 7117.0 37289.0 53475.0 61780.0 43413 34.7 0.4638 53138 31.1 10.276931
338 Kent County MI 05000US26081 -85.547446 43.032497 642173 514715.0 59715.0 18849.0 1930.0 67.0 21076.0 13897.0 23270.0 103835.0 129279.0 146606.0 75539 35.1 0.4573 59668 27.6 10.943842
339 Lapeer County MI 05000US26087 -83.224325 43.088633 88340 84352.0 1009.0 509.0 203.0 56.0 522.0 1150.0 4652.0 24180.0 20907.0 11095.0 10064 44.6 0.4330 54309 33.7 16.170354
340 Lenawee County MI 05000US26091 -84.066853 41.895915 98504 91291.0 2998.0 256.0 702.0 0.0 663.0 1116.0 4261.0 25187.0 21919.0 15097.0 11686 42.1 0.4336 51918 26.9 15.425743
341 Livingston County MI 05000US26093 -83.911718 42.602532 188624 182458.0 1273.0 1730.0 291.0 79.0 267.0 1062.0 4742.0 36223.0 43413.0 44858.0 10727 43.5 0.4164 78038 26.7 10.812720
342 Macomb County MI 05000US26099 -82.910869 42.671467 867730 706518.0 100689.0 32772.0 1762.0 50.0 4781.0 15331.0 37462.0 189808.0 208460.0 151316.0 93270 41.0 0.4290 60143 29.4 12.693460
343 Marquette County MI 05000US26103 -87.584028 46.656596 66435 61683.0 808.0 649.0 207.0 15.0 283.0 413.0 1300.0 13094.0 14036.0 14640.0 10108 38.4 0.4318 51275 32.7 17.229494
344 Midland County MI 05000US26111 -84.379219 43.648378 83462 77534.0 1235.0 1738.0 228.0 0.0 403.0 675.0 2446.0 16334.0 19114.0 19756.0 6711 42.6 0.4636 57269 27.2 15.566737
345 Monroe County MI 05000US26115 -83.487106 41.916097 149208 140808.0 4242.0 952.0 804.0 0.0 514.0 1152.0 7040.0 37166.0 37585.0 20139.0 14320 42.2 0.4146 60799 28.4 14.140400
346 Muskegon County MI 05000US26121 -86.751892 43.289258 173408 139261.0 24450.0 1128.0 1249.0 0.0 1092.0 1406.0 9256.0 41069.0 44723.0 19575.0 31910 39.1 0.4316 44264 29.8 18.908263
347 Oakland County MI 05000US26125 -83.384210 42.660452 1243970 934642.0 167411.0 87646.0 3016.0 232.0 11061.0 11112.0 35856.0 161799.0 255592.0 404076.0 104971 41.0 0.4757 71920 26.4 9.636146
348 Ottawa County MI 05000US26139 -86.655342 42.942346 282250 255093.0 3439.0 6244.0 628.0 165.0 7174.0 3754.0 7787.0 53335.0 52659.0 55773.0 25957 35.0 0.4069 64513 26.5 10.278317
349 Saginaw County MI 05000US26145 -84.055410 43.328267 192326 144965.0 35768.0 2503.0 493.0 61.0 2800.0 1332.0 10133.0 43655.0 44580.0 29891.0 32331 41.0 0.4522 45849 31.1 19.415497
350 St. Clair County MI 05000US26147 -82.668914 42.928804 159587 148831.0 3835.0 1223.0 358.0 0.0 1247.0 1675.0 9658.0 39983.0 41221.0 18456.0 21899 43.6 0.4293 51864 29.7 16.452246
351 Shiawassee County MI 05000US26155 -84.146352 42.951545 68554 NaN NaN NaN NaN NaN NaN 176.0 2392.0 17896.0 19573.0 7739.0 7392 41.8 0.4251 53244 27.8 17.794165
352 Van Buren County MI 05000US26159 -86.305696 42.283986 75223 64948.0 2312.0 425.0 1240.0 146.0 2211.0 1265.0 4310.0 17068.0 17780.0 10307.0 12872 40.7 0.4770 47917 27.3 22.803968
353 Washtenaw County MI 05000US26161 -83.844634 42.252327 364709 269158.0 43375.0 31232.0 1789.0 179.0 2218.0 1794.0 7360.0 33885.0 58372.0 124255.0 50914 33.5 0.4903 65601 30.7 8.351716
354 Wayne County MI 05000US26163 -83.261953 42.284664 1749366 924131.0 680630.0 54230.0 5458.0 420.0 36368.0 32912.0 115053.0 350222.0 384204.0 269403.0 391366 37.8 0.5010 43464 33.0 20.638415
355 Anoka County MN 05000US27003 -93.242723 45.274110 345957 291760.0 19779.0 15529.0 2480.0 195.0 5151.0 3481.0 9536.0 68923.0 87629.0 63583.0 22871 38.1 0.3960 76533 29.7 10.029492
356 Blue Earth County MN 05000US27013 -94.064071 44.038225 66441 NaN NaN NaN NaN NaN NaN 387.0 1480.0 11077.0 12410.0 12501.0 10188 30.5 0.4353 53869 29.7 11.764706
357 Carver County MN 05000US27019 -93.800575 44.821381 100262 NaN NaN NaN NaN NaN NaN 207.0 1206.0 11950.0 20706.0 31531.0 4038 38.0 0.4314 92455 24.1 7.285776
358 Dakota County MN 05000US27037 -93.062481 44.670893 417486 345463.0 24024.0 20140.0 1195.0 102.0 12277.0 4372.0 8901.0 55783.0 94348.0 115247.0 22262 37.6 0.4120 78662 28.0 8.183895
359 Hennepin County MN 05000US27053 -93.475185 45.006064 1232483 893871.0 158741.0 85906.0 9923.0 483.0 40359.0 20435.0 33098.0 141239.0 235715.0 415202.0 131859 36.3 0.4862 71200 28.3 11.148446
360 Olmsted County MN 05000US27109 -92.406722 44.003429 153102 129033.0 9005.0 8964.0 900.0 0.0 2226.0 1415.0 3325.0 20124.0 32869.0 43958.0 13844 37.4 0.4430 72428 30.2 10.399791
361 Ramsey County MN 05000US27123 -93.100141 45.015250 540649 358606.0 59737.0 76470.0 3089.0 209.0 15811.0 15252.0 15834.0 80089.0 96992.0 146659.0 72994 34.9 0.4610 60369 28.8 11.956808
362 Rice County MN 05000US27131 -93.298503 44.350943 65622 56157.0 3451.0 1404.0 86.0 0.0 2872.0 1351.0 3145.0 12397.0 13548.0 11417.0 5285 37.6 0.4123 69135 29.2 15.514304
363 St. Louis County MN 05000US27137 -92.463645 47.583852 199980 184278.0 3353.0 2228.0 4859.0 45.0 938.0 1021.0 6454.0 38594.0 51528.0 37201.0 29101 41.6 0.4483 49825 30.1 18.099096
364 Scott County MN 05000US27139 -93.534553 44.651932 143680 119736.0 4743.0 8060.0 681.0 0.0 5193.0 1861.0 1249.0 21944.0 29687.0 37087.0 9387 36.7 0.3974 88765 32.6 6.820490
365 Sherburne County MN 05000US27141 -93.775092 45.443171 93528 86674.0 2417.0 791.0 272.0 0.0 1213.0 326.0 2003.0 16808.0 24757.0 15564.0 5986 35.8 0.3586 83675 27.6 8.934566
366 Stearns County MN 05000US27145 -94.610482 45.555234 155652 138022.0 8669.0 3701.0 222.0 0.0 2483.0 1268.0 3548.0 27695.0 37060.0 24970.0 18223 34.4 0.4289 57728 25.3 10.728655
367 Washington County MN 05000US27163 -92.890117 45.037929 253117 216807.0 11366.0 12637.0 1002.0 66.0 3014.0 2150.0 4238.0 38142.0 53098.0 70527.0 10615 39.1 0.3968 90256 27.4 7.696138
368 Wright County MN 05000US27171 -93.966397 45.175091 132550 NaN NaN NaN NaN NaN NaN 1365.0 3111.0 22844.0 31160.0 26202.0 5952 36.6 0.3934 74190 28.7 10.514809
369 Boone County MO 05000US29019 -92.310779 38.989657 176594 142618.0 16256.0 9021.0 811.0 23.0 2821.0 1211.0 3543.0 22503.0 31306.0 46454.0 29321 30.6 0.4706 52752 28.6 9.862305
370 Buchanan County MO 05000US29021 -94.808173 39.660370 88938 77933.0 4572.0 1054.0 372.0 0.0 1607.0 1819.0 4773.0 20444.0 18539.0 13661.0 16133 37.9 0.4715 48550 26.7 22.554532
371 Cape Girardeau County MO 05000US29031 -89.684908 37.383882 78913 NaN NaN NaN NaN NaN NaN 456.0 3319.0 15670.0 15317.0 15545.0 12284 36.0 0.4691 50223 27.0 12.513613
372 Cass County MO 05000US29037 -94.354242 38.647159 102845 93928.0 3018.0 802.0 129.0 0.0 917.0 761.0 3092.0 23397.0 24977.0 16813.0 9171 40.0 0.3959 63420 30.4 14.621758
373 Christian County MO 05000US29043 -93.187614 36.969739 84401 NaN NaN NaN NaN NaN NaN 303.0 4443.0 15560.0 20877.0 14562.0 10091 37.3 0.4098 53172 30.2 11.891979
374 Clay County MO 05000US29047 -94.421502 39.315551 239085 204294.0 15923.0 6193.0 534.0 1184.0 4577.0 1492.0 6509.0 47246.0 49776.0 54652.0 20861 37.4 0.4087 65430 25.7 7.469966
375 Cole County MO 05000US29051 -92.271404 38.506847 76631 64560.0 8477.0 1107.0 290.0 39.0 254.0 537.0 2937.0 15435.0 14723.0 17376.0 7459 39.2 0.4010 55303 21.7 20.680113
376 Franklin County MO 05000US29071 -91.073410 38.408313 102838 NaN NaN NaN NaN NaN NaN 1151.0 4911.0 22643.0 24319.0 16526.0 11170 40.9 0.4325 55359 24.1 16.358094
377 Greene County MO 05000US29077 -93.340641 37.258196 288690 258094.0 10029.0 5704.0 2188.0 110.0 4975.0 2276.0 10013.0 54204.0 62586.0 55713.0 46951 36.0 0.4718 42062 29.0 14.022377
378 Jackson County MO 05000US29095 -94.342507 39.007233 691801 463423.0 161932.0 12585.0 3089.0 2433.0 25949.0 9918.0 27962.0 138405.0 141523.0 145605.0 106061 36.5 0.4690 50815 28.2 15.905076
379 Jasper County MO 05000US29097 -94.338869 37.200865 119111 107566.0 2615.0 1634.0 2263.0 139.0 2123.0 2421.0 6841.0 28050.0 23002.0 16349.0 21358 36.3 0.4517 45786 27.8 15.287557
380 Jefferson County MO 05000US29099 -90.543138 38.257414 224226 215622.0 2229.0 1882.0 580.0 0.0 365.0 1621.0 15788.0 48600.0 57655.0 28690.0 22710 39.3 0.3806 61559 27.4 12.393438
381 Platte County MO 05000US29165 -94.761472 39.378696 98309 84433.0 6635.0 2838.0 314.0 73.0 998.0 451.0 2164.0 13771.0 20969.0 28393.0 5435 38.3 0.4288 77581 25.7 8.666192
382 St. Charles County MO 05000US29183 -90.674915 38.781102 390918 352403.0 17540.0 9844.0 500.0 0.0 1809.0 1160.0 9518.0 63575.0 83715.0 104005.0 18592 38.0 0.3873 80644 24.7 9.147619
383 St. Francois County MO 05000US29187 -90.473868 37.810707 66627 NaN NaN NaN NaN NaN NaN 470.0 5062.0 18697.0 14932.0 6312.0 7296 39.7 0.4268 45431 29.4 20.365503
384 St. Louis County MO 05000US29189 -90.445954 38.640702 998581 684030.0 238612.0 39819.0 1834.0 271.0 8883.0 7747.0 31886.0 146304.0 196490.0 300433.0 87235 40.3 0.4859 62572 27.4 11.332578
385 St. Louis city MO 05000US29510 -90.244582 38.635699 311404 144752.0 145886.0 10264.0 762.0 88.0 3317.0 3688.0 21968.0 52086.0 63719.0 74608.0 72043 35.3 0.5310 40346 29.0 22.111767
386 DeSoto County MS 05000US28033 -89.993240 34.874266 175611 119958.0 46205.0 2663.0 416.0 0.0 3501.0 1914.0 7403.0 33075.0 42034.0 28670.0 18199 37.2 0.4243 64505 26.7 11.912056
387 Forrest County MS 05000US28035 -89.259447 31.188580 75979 NaN NaN NaN NaN NaN NaN 815.0 3437.0 13358.0 16853.0 11700.0 19704 31.9 0.5268 37275 30.7 23.572678
388 Harrison County MS 05000US28047 -89.083376 30.416536 203234 139366.0 49451.0 5702.0 1225.0 0.0 1604.0 2730.0 11987.0 39605.0 49857.0 26982.0 40990 35.6 0.4526 42770 31.3 20.276087
389 Hinds County MS 05000US28049 -90.465900 32.267924 241229 NaN NaN NaN NaN NaN NaN 3492.0 12602.0 38521.0 51710.0 45957.0 45159 34.4 0.4799 43657 31.0 18.013507
390 Jackson County MS 05000US28059 -88.619991 30.458491 141241 98840.0 31706.0 3863.0 506.0 0.0 4189.0 2269.0 6478.0 30424.0 32144.0 21252.0 25397 38.0 0.4463 54606 28.5 21.004319
391 Jones County MS 05000US28067 -89.167262 31.621044 67953 NaN NaN NaN NaN NaN NaN 1776.0 4133.0 12456.0 16464.0 8425.0 13370 36.8 0.4729 43350 35.7 39.018513
392 Lauderdale County MS 05000US28075 -88.660449 32.403998 77755 NaN NaN NaN NaN NaN NaN 1504.0 4431.0 15711.0 17630.0 11135.0 17469 37.8 0.4672 42364 28.0 26.840496
393 Lee County MS 05000US28081 -88.680887 34.288964 85381 NaN NaN NaN NaN NaN NaN 1074.0 7436.0 14652.0 19262.0 13538.0 14675 38.3 0.4626 42235 32.8 23.704417
394 Madison County MS 05000US28089 -90.034160 32.634370 105114 NaN NaN NaN NaN NaN NaN 649.0 4623.0 11152.0 19539.0 31861.0 12644 37.6 0.4774 64350 31.8 16.616559
395 Rankin County MS 05000US28121 -89.946552 32.262057 150228 NaN NaN NaN NaN NaN NaN 2165.0 7755.0 30290.0 30891.0 29790.0 10752 37.1 0.3843 62332 22.6 14.897166
396 Cascade County MT 05000US30013 -111.350571 47.316443 81755 72572.0 992.0 738.0 3673.0 142.0 325.0 548.0 3317.0 16572.0 21078.0 12943.0 11861 38.6 0.4703 45138 29.1 20.610800
397 Flathead County MT 05000US30029 -114.054319 48.314696 98082 92689.0 282.0 340.0 848.0 163.0 679.0 166.0 3542.0 19471.0 24851.0 21006.0 11585 42.3 0.4696 50142 34.9 12.737086
398 Gallatin County MT 05000US30031 -111.173443 45.535559 104502 NaN NaN NaN NaN NaN NaN 492.0 1816.0 11272.0 19703.0 32812.0 13242 33.7 0.5056 61211 29.1 11.542730
399 Lewis and Clark County MT 05000US30049 -112.382954 47.122133 67282 NaN NaN NaN NaN NaN NaN 43.0 1231.0 11718.0 16146.0 17726.0 6753 41.6 0.4590 63475 25.4 11.919510
400 Missoula County MT 05000US30063 -113.892681 47.027263 116130 106680.0 715.0 1855.0 3019.0 0.0 237.0 434.0 4447.0 15886.0 24861.0 32019.0 18709 36.4 0.4981 46550 30.0 12.804610
401 Yellowstone County MT 05000US30111 -108.276656 45.936987 158437 144305.0 829.0 959.0 6176.0 0.0 786.0 788.0 4647.0 35340.0 31265.0 34353.0 11353 38.9 0.4259 57945 26.0 13.111552
402 Alamance County NC 05000US37001 -79.399935 36.041974 159688 110933.0 31088.0 2245.0 779.0 307.0 11140.0 3756.0 9772.0 27697.0 41452.0 24330.0 25864 39.9 0.4336 45100 27.4 18.193540
403 Brunswick County NC 05000US37019 -78.227688 34.038708 126953 NaN NaN NaN NaN NaN NaN 610.0 6425.0 29090.0 35151.0 26984.0 18025 53.4 0.4748 50692 41.7 15.311701
404 Buncombe County NC 05000US37021 -82.530426 35.609371 256088 226953.0 15445.0 3542.0 959.0 425.0 3181.0 2741.0 12604.0 44306.0 54871.0 70529.0 32645 42.6 0.4630 50685 28.8 16.004538
405 Burke County NC 05000US37023 -81.706180 35.746182 88851 NaN NaN NaN NaN NaN NaN 2153.0 7015.0 20344.0 21355.0 10414.0 16523 45.0 0.4493 40345 26.8 26.568402
406 Cabarrus County NC 05000US37025 -80.552868 35.387845 201590 141332.0 34402.0 6212.0 511.0 374.0 12541.0 3429.0 8755.0 30160.0 45635.0 42254.0 23557 37.7 0.4151 63386 24.9 8.766575
407 Caldwell County NC 05000US37027 -81.530076 35.957857 81449 NaN NaN NaN NaN NaN NaN 1233.0 9060.0 19544.0 18501.0 7932.0 14100 43.3 0.4547 36301 30.7 22.354228
408 Carteret County NC 05000US37031 -76.526967 34.858313 68890 61844.0 3798.0 546.0 285.0 0.0 320.0 1409.0 2103.0 15026.0 19238.0 13237.0 7411 48.0 0.4404 51206 26.1 16.589108
409 Catawba County NC 05000US37035 -81.214151 35.663182 156459 122004.0 13875.0 6466.0 600.0 0.0 10980.0 2957.0 10828.0 33011.0 37069.0 22744.0 18490 41.3 0.4415 48913 25.7 16.227401
410 Chatham County NC 05000US37037 -79.251454 35.704994 72243 59488.0 8033.0 868.0 0.0 0.0 1187.0 2335.0 3461.0 10641.0 13178.0 22522.0 9732 47.1 0.5104 62961 31.7 13.728647
411 Cleveland County NC 05000US37045 -81.557114 35.334630 97144 NaN NaN NaN NaN NaN NaN 1496.0 7636.0 19771.0 22150.0 13084.0 20671 40.7 0.4851 36992 29.9 29.193792
412 Craven County NC 05000US37049 -77.082541 35.118179 103445 71552.0 20612.0 3215.0 812.0 50.0 3030.0 1861.0 5461.0 16131.0 24892.0 17874.0 15114 37.4 0.4605 49938 29.0 14.601583
413 Cumberland County NC 05000US37051 -78.828719 35.050192 327127 162910.0 124566.0 7649.0 5012.0 737.0 7461.0 3647.0 12547.0 52864.0 80345.0 51833.0 59784 32.0 0.4403 45205 29.9 14.531424
414 Davidson County NC 05000US37057 -80.206525 35.795122 164926 139762.0 13621.0 2630.0 133.0 0.0 4359.0 3117.0 12625.0 38479.0 37457.0 19956.0 25583 42.3 0.4338 45678 25.9 17.175213
415 Durham County NC 05000US37063 -78.877919 36.036589 306212 158099.0 113137.0 15714.0 669.0 267.0 10281.0 7603.0 15300.0 37252.0 46339.0 100302.0 47869 35.1 0.4808 53832 30.0 12.566190
416 Forsyth County NC 05000US37067 -80.257289 36.131667 371511 245900.0 97584.0 8532.0 1513.0 21.0 8879.0 7991.0 15271.0 61050.0 75172.0 85056.0 65062 38.0 0.5159 48271 30.0 16.663361
417 Gaston County NC 05000US37071 -81.177256 35.293344 216965 167912.0 34358.0 3627.0 1004.0 0.0 5169.0 3646.0 16966.0 44953.0 48133.0 32406.0 33374 39.8 0.4933 48711 27.9 16.345714
418 Guilford County NC 05000US37081 -79.788665 36.079065 521330 290962.0 175229.0 26138.0 1975.0 383.0 14980.0 10780.0 24124.0 85861.0 99670.0 119884.0 93700 37.1 0.4984 47262 30.0 16.580985
419 Harnett County NC 05000US37085 -78.871610 35.368635 130881 86920.0 27503.0 916.0 878.0 202.0 8723.0 2757.0 6929.0 24954.0 31725.0 16735.0 20298 34.0 0.4151 51637 28.8 17.834395
420 Henderson County NC 05000US37089 -82.479634 35.336424 114209 NaN NaN NaN NaN NaN NaN 2799.0 5978.0 22277.0 27022.0 25949.0 12990 47.3 0.4303 54963 27.3 15.071875
421 Iredell County NC 05000US37097 -80.874545 35.806356 172916 142713.0 21633.0 4410.0 194.0 0.0 1728.0 2774.0 8661.0 34785.0 37520.0 31022.0 18051 40.8 0.4764 55891 24.7 15.008380
422 Johnston County NC 05000US37101 -78.367267 35.513405 191450 149935.0 31782.0 536.0 729.0 0.0 3042.0 3825.0 11455.0 33917.0 43908.0 30060.0 24672 38.0 0.3930 54719 28.0 19.650134
423 Lincoln County NC 05000US37109 -81.225176 35.487825 81168 NaN NaN NaN NaN NaN NaN 910.0 4835.0 18667.0 20540.0 12202.0 10797 44.3 0.4423 49453 28.3 16.962138
424 Mecklenburg County NC 05000US37119 -80.833832 35.246862 1054835 574931.0 334621.0 59544.0 2740.0 786.0 55771.0 20475.0 37063.0 121213.0 202835.0 313875.0 126156 35.0 0.4780 62978 28.0 10.661971
425 Moore County NC 05000US37125 -79.480664 35.310163 95776 NaN NaN NaN NaN NaN NaN 1358.0 3043.0 15082.0 22965.0 26150.0 9657 45.4 0.4585 52279 24.0 15.644820
426 Nash County NC 05000US37127 -77.987555 35.965945 94005 50212.0 37748.0 845.0 434.0 0.0 3161.0 1703.0 7781.0 22527.0 19068.0 13445.0 14090 41.8 0.4855 47902 26.0 22.994154
427 New Hanover County NC 05000US37129 -77.871378 34.177466 223483 180970.0 31080.0 2161.0 625.0 0.0 2255.0 2160.0 9258.0 35436.0 45587.0 59649.0 40337 39.1 0.5125 50028 32.6 13.455737
428 Onslow County NC 05000US37133 -77.503297 34.763460 187136 139353.0 24311.0 3350.0 591.0 413.0 3224.0 1421.0 7133.0 29459.0 43180.0 18705.0 22662 26.6 0.3787 47150 27.8 11.673089
429 Orange County NC 05000US37135 -79.119355 36.062499 141796 106590.0 14958.0 11270.0 677.0 0.0 1976.0 2677.0 3779.0 11272.0 18149.0 50931.0 16820 34.1 0.4949 66423 31.8 8.903850
430 Pitt County NC 05000US37147 -77.372404 35.591065 177220 102197.0 62515.0 2983.0 640.0 64.0 4316.0 2159.0 7293.0 24107.0 38080.0 33640.0 36936 32.1 0.4907 46573 31.8 14.657450
431 Randolph County NC 05000US37151 -79.806215 35.709915 143416 119977.0 9771.0 1463.0 262.0 0.0 9133.0 3613.0 11962.0 32028.0 32344.0 16173.0 20046 42.6 0.4036 44836 27.6 21.779092
432 Robeson County NC 05000US37155 -79.100881 34.639210 133235 38442.0 32385.0 958.0 52789.0 197.0 6244.0 4174.0 12590.0 30024.0 25345.0 11328.0 35298 36.3 0.4844 34254 26.4 43.097755
433 Rockingham County NC 05000US37157 -79.782889 36.380927 91393 NaN NaN NaN NaN NaN NaN 1690.0 9507.0 21975.0 20959.0 9321.0 16752 43.7 0.4349 40985 26.3 27.293452
434 Rowan County NC 05000US37159 -80.525344 35.639218 139933 107453.0 22460.0 1104.0 659.0 0.0 5519.0 2114.0 8495.0 30628.0 33023.0 18944.0 22367 39.4 0.4447 48379 24.9 19.899079
435 Rutherford County NC 05000US37161 -81.919582 35.402747 66421 NaN NaN NaN NaN NaN NaN 1920.0 5286.0 15631.0 14798.0 8617.0 11156 45.6 0.4511 36121 25.5 26.643688
436 Surry County NC 05000US37171 -80.686463 36.415416 72113 NaN NaN NaN NaN NaN NaN 2753.0 6387.0 15174.0 16067.0 7635.0 11826 44.3 0.5322 36802 31.1 23.661583
437 Union County NC 05000US37179 -80.530131 34.991501 226606 187188.0 25691.0 5792.0 528.0 0.0 2272.0 4014.0 12280.0 35366.0 42185.0 48761.0 20041 38.4 0.4739 71588 29.4 8.175167
438 Wake County NC 05000US37183 -78.650624 35.789846 1046791 688226.0 213306.0 69541.0 3815.0 157.0 40616.0 14972.0 26186.0 98411.0 177501.0 368828.0 93252 36.0 0.4458 76097 26.7 6.130938
439 Wayne County NC 05000US37191 -78.004826 35.362741 124150 79743.0 37003.0 1539.0 324.0 65.0 1082.0 4286.0 7922.0 24852.0 29484.0 14509.0 26588 37.3 0.4755 41711 30.3 21.850127
440 Wilkes County NC 05000US37193 -81.165354 36.209303 68740 NaN NaN NaN NaN NaN NaN 1254.0 5568.0 16286.0 16635.0 7407.0 10424 44.2 0.4259 41332 29.0 22.512252
441 Wilson County NC 05000US37195 -77.918982 35.704125 81661 NaN NaN NaN NaN NaN NaN 3182.0 6631.0 18832.0 14808.0 10220.0 18167 40.3 0.4998 39036 32.0 24.895621
442 Burleigh County ND 05000US38015 -100.462001 46.971843 94487 NaN NaN NaN NaN NaN NaN 291.0 3421.0 14493.0 22746.0 21090.0 6523 36.5 0.4396 64558 23.0 13.417786
443 Cass County ND 05000US38017 -97.252375 46.927003 175249 153678.0 9281.0 4496.0 1478.0 0.0 908.0 1204.0 3828.0 20677.0 38033.0 45102.0 19764 32.4 0.4404 59240 25.6 9.316852
444 Grand Forks County ND 05000US38035 -97.450851 47.926003 71083 NaN NaN NaN NaN NaN NaN 167.0 1735.0 11376.0 14646.0 13996.0 10730 29.9 0.4599 50008 30.3 12.790814
445 Ward County ND 05000US38101 -101.540537 48.216686 70210 60834.0 1578.0 865.0 3000.0 0.0 1717.0 175.0 2062.0 14349.0 14356.0 12236.0 5647 30.7 0.4151 62573 29.2 6.682732
446 Douglas County NE 05000US31055 -96.154066 41.297091 554995 444192.0 61497.0 19640.0 1527.0 390.0 11142.0 14833.0 18563.0 79874.0 105217.0 137940.0 67359 34.5 0.4654 58990 29.4 13.705605
447 Lancaster County NE 05000US31109 -96.688658 40.783547 309637 267852.0 12445.0 12252.0 1952.0 3.0 4680.0 3717.0 7766.0 40628.0 60673.0 77214.0 37323 33.1 0.4567 58845 28.5 10.098585
448 Sarpy County NE 05000US31153 -96.109125 41.115063 179023 158913.0 6658.0 4776.0 595.0 168.0 2121.0 1719.0 3654.0 25466.0 37697.0 44947.0 10743 34.7 0.3843 73569 26.6 9.331977
449 Cheshire County NH 05000US33005 -72.248217 42.925455 75774 72373.0 520.0 1122.0 148.0 0.0 197.0 682.0 2954.0 17187.0 13961.0 17451.0 4994 42.9 0.4382 56364 30.8 14.591545
450 Grafton County NH 05000US33009 -71.842264 43.926488 88888 NaN NaN NaN NaN NaN NaN 320.0 3157.0 16606.0 14423.0 27601.0 9008 43.1 0.5077 61520 29.0 13.825791
451 Hillsborough County NH 05000US33011 -71.723101 42.911643 407761 367574.0 9702.0 14874.0 467.0 32.0 5283.0 3561.0 16251.0 72925.0 82398.0 106696.0 32563 40.4 0.4186 76254 27.8 9.200955
452 Merrimack County NH 05000US33013 -71.680130 43.299485 148582 140747.0 836.0 2563.0 354.0 0.0 209.0 810.0 5235.0 27866.0 32581.0 37811.0 9663 42.4 0.4185 69505 28.2 10.484782
453 Rockingham County NH 05000US33015 -71.099437 42.989360 303251 288104.0 2089.0 5846.0 57.0 0.0 1712.0 1695.0 7455.0 55536.0 64475.0 88648.0 10865 44.6 0.4134 81726 26.0 7.824510
454 Strafford County NH 05000US33017 -71.035927 43.293177 127428 118919.0 1196.0 4107.0 50.0 0.0 175.0 449.0 3463.0 22465.0 26634.0 29045.0 7332 36.9 0.4070 71295 25.0 11.339993
455 Atlantic County NJ 05000US34001 -74.633758 39.469354 270991 176909.0 41557.0 22599.0 824.0 60.0 20986.0 5476.0 13289.0 61796.0 51912.0 51545.0 38021 40.6 0.4599 56778 33.0 17.214834
456 Bergen County NJ 05000US34003 -74.074522 40.959090 939151 678573.0 56391.0 153276.0 1748.0 804.0 20616.0 20043.0 24347.0 147460.0 136290.0 325027.0 63589 41.4 0.4705 93683 29.9 8.823137
457 Burlington County NJ 05000US34005 -74.663006 39.875786 449284 323113.0 74530.0 23697.0 378.0 123.0 10123.0 3716.0 13659.0 93685.0 84528.0 117207.0 25271 41.5 0.4359 80254 29.8 9.893767
458 Camden County NJ 05000US34007 -74.961251 39.802352 510150 324529.0 96342.0 29696.0 1032.0 0.0 44228.0 9797.0 25365.0 106532.0 94158.0 109647.0 59392 38.5 0.4676 66362 31.4 13.428242
459 Cape May County NJ 05000US34009 -74.847716 39.086143 94430 NaN NaN NaN NaN NaN NaN 1310.0 3930.0 22479.0 19412.0 22223.0 11002 49.0 0.4815 62548 38.8 12.477182
460 Cumberland County NJ 05000US34011 -75.121644 39.328387 153797 103640.0 28739.0 2258.0 1960.0 0.0 10350.0 6837.0 13298.0 40408.0 25704.0 14432.0 27031 36.8 0.4455 49110 38.0 21.164865
461 Essex County NJ 05000US34013 -74.246136 40.787216 796914 355482.0 317198.0 42298.0 1518.0 0.0 58747.0 27017.0 37024.0 148768.0 127211.0 182368.0 127170 37.2 0.5565 54277 33.4 17.856049
462 Gloucester County NJ 05000US34015 -75.143708 39.721019 292330 237650.0 29805.0 8955.0 215.0 0.0 6186.0 3824.0 9926.0 68480.0 54665.0 62232.0 21758 39.7 0.4097 79879 30.6 10.172047
463 Hudson County NJ 05000US34017 -74.078627 40.731384 677983 367911.0 81508.0 102554.0 2484.0 752.0 96113.0 34190.0 28839.0 127934.0 84016.0 196020.0 104668 34.9 0.5084 63808 28.4 13.378678
464 Hunterdon County NJ 05000US34019 -74.911969 40.565283 124676 NaN NaN NaN NaN NaN NaN 1199.0 2724.0 19790.0 17615.0 46318.0 5694 46.2 0.4371 113684 31.8 7.689374
465 Mercer County NJ 05000US34021 -74.703724 40.282503 371023 238907.0 78453.0 40864.0 493.0 597.0 4213.0 11318.0 12975.0 63215.0 52544.0 106660.0 39443 38.5 0.5003 77650 32.0 15.523798
466 Middlesex County NJ 05000US34023 -74.407585 40.439592 837073 490322.0 83900.0 203045.0 2096.0 297.0 35789.0 19645.0 30188.0 152177.0 122684.0 242378.0 69774 38.2 0.4305 82375 28.8 9.272336
467 Monmouth County NJ 05000US34025 -74.152446 40.287056 625846 514689.0 45430.0 35774.0 966.0 30.0 18484.0 9590.0 19044.0 105325.0 104792.0 192457.0 42572 43.0 0.4670 90226 32.6 9.183209
468 Morris County NJ 05000US34027 -74.547427 40.858581 498423 402032.0 15796.0 53056.0 139.0 0.0 17716.0 8285.0 11168.0 70570.0 70205.0 184647.0 26458 42.4 0.4542 106985 27.4 6.818772
469 Ocean County NJ 05000US34029 -74.263027 39.865850 592497 536665.0 17336.0 11908.0 400.0 664.0 17161.0 7749.0 25063.0 136881.0 116812.0 117001.0 65390 42.8 0.4514 62222 37.0 16.585231
470 Passaic County NJ 05000US34031 -74.300308 41.033763 507945 333685.0 57390.0 26481.0 1643.0 180.0 74754.0 19956.0 28157.0 110652.0 69555.0 93237.0 87856 36.7 0.4780 62016 33.9 18.742524
471 Salem County NJ 05000US34033 -75.357356 39.573828 63436 50303.0 8147.0 774.0 162.0 0.0 1966.0 919.0 4395.0 17344.0 12545.0 8217.0 8287 41.2 0.4608 53662 33.1 22.430254
472 Somerset County NJ 05000US34035 -74.619930 40.565522 333751 224326.0 32448.0 57437.0 1718.0 47.0 10493.0 6072.0 8487.0 48748.0 46221.0 118651.0 16395 41.5 0.4574 104478 28.5 7.030275
473 Sussex County NJ 05000US34037 -74.691855 41.137424 142522 132461.0 2904.0 3229.0 0.0 0.0 1717.0 1016.0 3797.0 32778.0 28771.0 34509.0 8508 44.7 0.4099 87829 31.3 8.353534
474 Union County NJ 05000US34039 -74.308696 40.659871 555630 304604.0 114911.0 27892.0 2210.0 0.0 89983.0 21374.0 23500.0 107616.0 84409.0 131326.0 58356 38.5 0.5060 72028 31.6 13.358734
475 Warren County NJ 05000US34041 -75.009542 40.853524 106617 95037.0 4981.0 2825.0 117.0 0.0 2019.0 2627.0 4319.0 23435.0 20572.0 23700.0 9278 44.1 0.4152 74867 29.2 10.495656
476 Bernalillo County NM 05000US35001 -106.669065 35.054002 676953 479588.0 18679.0 18595.0 34521.0 498.0 101451.0 15166.0 31460.0 109164.0 147108.0 155675.0 110517 37.4 0.4730 50601 30.9 16.235400
477 Chaves County NM 05000US35005 -104.469837 33.361604 65282 NaN NaN NaN NaN NaN NaN 4307.0 4607.0 10447.0 11332.0 9123.0 14534 35.1 0.4811 40960 26.5 28.308758
478 Doña Ana County NM 05000US35013 -106.832182 32.350912 214207 194538.0 3599.0 2341.0 2578.0 69.0 8652.0 11757.0 10972.0 30960.0 39175.0 34011.0 56426 33.5 0.4912 37496 31.1 24.609617
479 Lea County NM 05000US35025 -103.413271 32.795687 69749 62201.0 2172.0 0.0 452.0 46.0 3134.0 5819.0 5474.0 12300.0 10986.0 5961.0 14352 31.5 0.4445 55636 22.9 21.584187
480 McKinley County NM 05000US35031 -108.255307 35.573732 74923 NaN NaN NaN NaN NaN NaN 2471.0 7363.0 15187.0 13109.0 4374.0 27363 31.6 0.4899 31526 24.7 46.812223
481 Otero County NM 05000US35035 -105.781078 32.588776 65410 50841.0 2625.0 969.0 4423.0 0.0 3628.0 1472.0 3841.0 12436.0 16968.0 7790.0 13261 35.8 0.5330 43646 23.7 17.324875
482 Sandoval County NM 05000US35043 -106.882618 35.685073 142025 99399.0 2980.0 2100.0 18026.0 25.0 11878.0 2191.0 7989.0 21426.0 34181.0 30855.0 22909 38.8 0.4236 57158 32.0 16.007306
483 San Juan County NM 05000US35045 -108.324578 36.511624 115079 62316.0 656.0 690.0 41426.0 74.0 6795.0 2403.0 6546.0 21690.0 30389.0 12547.0 19154 36.3 0.4229 52003 23.0 33.464832
484 Santa Fe County NM 05000US35049 -105.966441 35.513722 148651 126078.0 1260.0 1976.0 5173.0 38.0 9950.0 3914.0 5524.0 26507.0 26070.0 45718.0 20939 46.0 0.4794 57863 26.7 18.698416
485 Valencia County NM 05000US35061 -106.806582 34.716840 75626 60577.0 788.0 340.0 2782.0 0.0 8365.0 1608.0 6200.0 17246.0 16503.0 9159.0 13418 38.3 0.4164 43700 31.5 28.398871
486 Clark County NV 05000US32003 -115.013819 36.214236 2155664 1330054.0 247427.0 211925.0 13583.0 17783.0 223704.0 79424.0 117030.0 434379.0 476888.0 340777.0 302789 37.0 0.4563 54384 30.6 16.305686
487 Washoe County NV 05000US32031 -119.710315 40.703311 453616 355315.0 10627.0 25562.0 6826.0 2436.0 32942.0 13418.0 21828.0 76677.0 105659.0 91097.0 54745 38.2 0.4741 58175 29.4 13.798175
488 Albany County NY 05000US36001 -73.974014 42.588271 308846 230831.0 38386.0 21011.0 788.0 0.0 5538.0 3916.0 12078.0 46857.0 53402.0 87343.0 35650 37.9 0.4635 61754 29.6 12.602031
489 Bronx County NY 05000US36005 -73.852939 40.848711 1455720 312877.0 495031.0 51958.0 8777.0 372.0 528863.0 95244.0 130651.0 266034.0 233297.0 170921.0 407377 33.6 0.4926 37525 34.9 21.305053
490 Broome County NY 05000US36007 -75.830291 42.161977 195334 166572.0 12650.0 7006.0 13.0 21.0 1866.0 2417.0 9308.0 40350.0 38591.0 38620.0 30474 39.6 0.4657 50463 33.1 14.862845
491 Cattaraugus County NY 05000US36009 -78.681006 42.244853 77677 71286.0 1148.0 509.0 2615.0 0.0 376.0 1040.0 4332.0 20809.0 15639.0 9896.0 10508 41.6 0.4167 46842 27.0 22.452163
492 Cayuga County NY 05000US36011 -76.574587 43.008546 77861 NaN NaN NaN NaN NaN NaN 861.0 5524.0 17112.0 17667.0 12731.0 8372 41.4 0.4138 54995 27.0 18.077892
493 Chautauqua County NY 05000US36013 -79.407595 42.304216 129504 120008.0 3784.0 760.0 326.0 64.0 2035.0 1653.0 7110.0 32249.0 27233.0 19214.0 24899 42.2 0.4303 42204 30.8 20.065758
494 Chemung County NY 05000US36015 -76.747179 42.155281 86322 75851.0 4798.0 1588.0 171.0 22.0 770.0 1162.0 3196.0 20103.0 18312.0 16578.0 11262 41.5 0.4492 51269 30.9 15.718519
495 Clinton County NY 05000US36019 -73.705648 44.752710 81073 NaN NaN NaN NaN NaN NaN 1211.0 4416.0 21300.0 16463.0 11902.0 9985 39.2 0.4077 55316 28.4 16.297675
496 Dutchess County NY 05000US36027 -73.739951 41.755009 294473 232515.0 31588.0 10743.0 989.0 422.0 9189.0 3652.0 15775.0 53669.0 58342.0 70870.0 24873 41.8 0.4587 74115 33.1 9.334566
497 Erie County NY 05000US36029 -78.778192 42.752759 921046 718970.0 122691.0 34132.0 4730.0 405.0 18740.0 10762.0 38779.0 181997.0 192839.0 213785.0 127064 40.0 0.4723 54246 29.5 15.971436
498 Jefferson County NY 05000US36045 -76.053522 43.995371 114006 98583.0 5871.0 1998.0 1667.0 41.0 1425.0 1070.0 3885.0 27174.0 24037.0 14883.0 17083 33.1 0.4343 45624 30.4 14.811059
499 Kings County NY 05000US36047 -73.950777 40.635133 2629150 1124155.0 861749.0 315850.0 8714.0 1402.0 237591.0 115173.0 176092.0 450548.0 353301.0 651985.0 536846 34.8 0.5252 55150 32.3 17.715483
500 Livingston County NY 05000US36051 -77.769779 42.727484 64257 59522.0 1985.0 699.0 352.0 76.0 800.0 326.0 3790.0 14264.0 12995.0 10841.0 8542 40.7 0.3850 53121 29.0 12.918340
501 Madison County NY 05000US36053 -75.663575 42.910026 71329 67495.0 1272.0 726.0 203.0 152.0 200.0 688.0 3141.0 16434.0 13395.0 13987.0 5456 41.3 0.4330 60630 28.4 17.843199
502 Monroe County NY 05000US36055 -77.664656 43.464475 747727 570709.0 113806.0 26420.0 2763.0 309.0 12100.0 10822.0 34667.0 116117.0 153586.0 191689.0 108069 38.7 0.4704 54492 31.9 13.607866
503 Nassau County NY 05000US36059 -73.589384 40.729687 1361500 926594.0 158561.0 127934.0 3420.0 285.0 102363.0 35671.0 33808.0 217826.0 220785.0 423371.0 79322 41.5 0.4578 105870 32.7 8.294520
504 New York County NY 05000US36061 -73.970174 40.776557 1643734 931953.0 246095.0 204434.0 4555.0 1205.0 179384.0 66463.0 70662.0 158391.0 176052.0 755940.0 275840 37.1 0.5945 77559 28.1 13.225301
505 Niagara County NY 05000US36063 -78.792143 43.456731 211758 185171.0 13450.0 2082.0 2246.0 141.0 1822.0 2109.0 9344.0 51170.0 50068.0 36715.0 25684 43.4 0.4245 50233 27.6 18.992695
506 Oneida County NY 05000US36065 -75.434282 43.242727 231190 197816.0 13313.0 9824.0 400.0 53.0 2474.0 4129.0 12995.0 51483.0 45886.0 42468.0 35168 41.1 0.4519 52996 26.9 16.003821
507 Onondaga County NY 05000US36067 -76.196117 43.006530 466194 370920.0 52781.0 17413.0 1769.0 81.0 6250.0 6571.0 19689.0 79703.0 96216.0 110393.0 66380 38.7 0.4653 56991 28.5 16.157697
508 Ontario County NY 05000US36069 -77.303277 42.856695 109828 101231.0 3180.0 1345.0 584.0 114.0 1649.0 1060.0 3715.0 21960.0 24107.0 26228.0 9893 44.3 0.4334 57448 30.8 11.534291
509 Orange County NY 05000US36071 -74.306252 41.402410 379210 278706.0 38853.0 10686.0 1877.0 149.0 32984.0 7502.0 15919.0 71627.0 74219.0 70091.0 46300 37.2 0.4395 73025 32.2 16.832710
510 Oswego County NY 05000US36075 -76.209258 43.461443 118987 113860.0 1795.0 730.0 184.0 75.0 167.0 972.0 7435.0 30988.0 24513.0 14223.0 20266 40.1 0.4212 53562 32.1 13.136020
511 Putnam County NY 05000US36079 -73.743882 41.427903 98900 85102.0 1490.0 2214.0 859.0 56.0 7046.0 2746.0 2290.0 17818.0 17031.0 30585.0 5685 43.8 0.4237 96992 31.7 4.594097
512 Queens County NY 05000US36081 -73.837929 40.658557 2333054 905860.0 421307.0 592922.0 9509.0 1240.0 318657.0 135253.0 138116.0 463579.0 369631.0 522093.0 303895 38.3 0.4564 62207 33.2 15.859148
513 Rensselaer County NY 05000US36083 -73.513845 42.710421 160070 137956.0 9755.0 4019.0 799.0 0.0 1866.0 2522.0 5690.0 29778.0 34629.0 36634.0 18023 39.5 0.4264 65965 27.6 13.034896
514 Richmond County NY 05000US36085 -74.137063 40.563855 476015 352530.0 48920.0 40747.0 251.0 721.0 22464.0 12602.0 22067.0 100582.0 84304.0 107267.0 62239 39.7 0.4580 77197 33.8 16.483149
515 Rockland County NY 05000US36087 -74.024772 41.154785 326780 225848.0 40350.0 19686.0 592.0 49.0 33989.0 12081.0 14296.0 44460.0 50101.0 81219.0 47860 36.5 0.4643 85515 37.6 18.084367
516 St. Lawrence County NY 05000US36089 -75.074311 44.488112 110038 102840.0 2925.0 1489.0 437.0 11.0 472.0 883.0 6443.0 25470.0 20783.0 16887.0 16475 38.8 0.4378 51592 27.1 16.509795
517 Saratoga County NY 05000US36091 -73.855387 43.106135 227053 211505.0 2719.0 6797.0 484.0 91.0 907.0 1857.0 6924.0 39007.0 45963.0 65271.0 12776 42.0 0.4455 76097 25.1 9.254773
518 Schenectady County NY 05000US36093 -74.043583 42.817542 154553 119476.0 14509.0 6739.0 258.0 219.0 7110.0 2429.0 7426.0 34772.0 27632.0 32144.0 14771 40.1 0.4518 58331 31.2 11.586617
519 Steuben County NY 05000US36101 -77.385525 42.266725 96940 91500.0 1835.0 1298.0 120.0 0.0 524.0 721.0 5018.0 24601.0 20770.0 15467.0 11689 42.3 0.4569 50575 25.5 16.516720
520 Suffolk County NY 05000US36103 -72.692218 40.943554 1492583 1195796.0 111177.0 58726.0 3361.0 833.0 93130.0 31760.0 53057.0 298638.0 278375.0 359095.0 106175 41.2 0.4433 92933 35.4 9.643673
521 Sullivan County NY 05000US36105 -74.764680 41.720176 74801 NaN NaN NaN NaN NaN NaN 1736.0 4974.0 15843.0 16922.0 11500.0 10852 43.8 0.4915 50652 30.6 16.759219
522 Tompkins County NY 05000US36109 -76.473712 42.453281 104871 83423.0 4698.0 10625.0 268.0 0.0 1656.0 445.0 2604.0 13082.0 12885.0 31343.0 18347 30.6 0.4750 56349 38.8 7.555131
523 Ulster County NY 05000US36111 -74.265447 41.947232 179225 152042.0 10859.0 3969.0 151.0 21.0 5275.0 3048.0 7130.0 38601.0 36063.0 42436.0 24059 44.1 0.4610 62790 34.3 11.186272
524 Warren County NY 05000US36113 -73.838139 43.555105 64567 NaN NaN NaN NaN NaN NaN 510.0 2436.0 16120.0 13823.0 14334.0 7473 47.1 0.4397 58061 30.3 15.564648
525 Wayne County NY 05000US36117 -77.063164 43.458758 90798 NaN NaN NaN NaN NaN NaN 1355.0 4074.0 20999.0 22531.0 13885.0 8312 43.6 0.3927 59538 26.1 13.820141
526 Westchester County NY 05000US36119 -73.745912 41.152770 974542 628687.0 144510.0 57962.0 2745.0 192.0 107885.0 34299.0 39242.0 129113.0 135204.0 317447.0 94613 40.7 0.5395 89709 32.3 10.937244
527 Allen County OH 05000US39003 -84.106546 40.771528 103742 NaN NaN NaN NaN NaN NaN 643.0 7003.0 25853.0 23140.0 11378.0 14916 38.2 0.4683 47592 29.1 17.605422
528 Ashtabula County OH 05000US39007 -80.745641 41.906644 98231 NaN NaN NaN NaN NaN NaN 1158.0 6422.0 29387.0 19749.0 9353.0 17613 43.4 0.4635 42965 31.6 22.105453
529 Athens County OH 05000US39009 -82.046008 39.333848 66186 NaN NaN NaN NaN NaN NaN 640.0 3574.0 13088.0 8906.0 9331.0 16283 27.9 0.4999 36193 30.0 17.076201
530 Belmont County OH 05000US39013 -80.967727 40.017682 68673 NaN NaN NaN NaN NaN NaN 588.0 3639.0 19981.0 15876.0 9679.0 10051 44.5 0.4583 48220 26.6 24.045521
531 Butler County OH 05000US39017 -84.565397 39.439915 377537 322458.0 30413.0 10086.0 536.0 98.0 3219.0 3758.0 17979.0 77595.0 67478.0 71670.0 45591 36.5 0.4410 63273 27.5 11.132150
532 Clark County OH 05000US39023 -83.783676 39.917032 134786 117082.0 9921.0 393.0 289.0 53.0 808.0 654.0 9272.0 35795.0 29195.0 16491.0 20474 41.9 0.4374 46811 28.3 16.645890
533 Clermont County OH 05000US39025 -84.149614 39.052084 203022 NaN NaN NaN NaN NaN NaN 2648.0 11791.0 43477.0 39486.0 39137.0 22135 39.7 0.4489 60661 27.8 13.763234
534 Columbiana County OH 05000US39029 -80.777231 40.768462 103685 97992.0 2766.0 0.0 201.0 0.0 1185.0 1168.0 6630.0 32453.0 23781.0 9783.0 18398 44.2 0.4132 47864 25.7 19.199884
535 Cuyahoga County OH 05000US39035 -81.724217 41.760392 1249352 780502.0 367135.0 35357.0 2275.0 389.0 21144.0 12355.0 65203.0 252767.0 259575.0 271844.0 221528 40.2 0.5163 46601 29.9 19.232533
536 Delaware County OH 05000US39041 -83.007462 40.278941 196463 173006.0 6944.0 11311.0 253.0 53.0 738.0 999.0 3494.0 25715.0 27274.0 70232.0 9934 38.1 0.4478 101693 24.9 5.242141
537 Erie County OH 05000US39043 -82.525897 41.554006 75107 NaN NaN NaN NaN NaN NaN 391.0 3588.0 20144.0 17534.0 11619.0 8844 46.2 0.4725 48949 28.6 16.159312
538 Fairfield County OH 05000US39045 -82.628276 39.752935 152597 133473.0 11068.0 1560.0 407.0 0.0 1309.0 1094.0 5594.0 33519.0 33900.0 28214.0 14992 40.2 0.4207 65316 27.6 10.630417
539 Franklin County OH 05000US39049 -83.008258 39.969447 1264518 855234.0 280522.0 62789.0 2170.0 747.0 15363.0 16185.0 54211.0 214836.0 220243.0 329161.0 205259 34.0 0.4644 56055 27.6 11.262755
540 Geauga County OH 05000US39055 -81.173505 41.499322 94060 NaN NaN NaN NaN NaN NaN 887.0 1550.0 16813.0 16813.0 23217.0 4260 44.9 0.4212 76384 28.0 17.841155
541 Greene County OH 05000US39057 -83.894894 39.687478 164765 141014.0 11672.0 4902.0 235.0 0.0 1707.0 1216.0 4664.0 27002.0 35309.0 40591.0 20151 38.6 0.4557 62018 26.4 9.926770
542 Hamilton County OH 05000US39061 -84.544187 39.196927 809099 542878.0 208972.0 21398.0 800.0 85.0 11962.0 9642.0 34660.0 145662.0 150053.0 201314.0 125214 37.0 0.5009 53229 27.8 15.253329
543 Hancock County OH 05000US39063 -83.666033 41.000471 75872 NaN NaN NaN NaN NaN NaN 465.0 2109.0 18555.0 16879.0 13260.0 7457 38.2 0.4264 52810 25.4 12.415991
544 Jefferson County OH 05000US39081 -80.761410 40.399188 66704 NaN NaN NaN NaN NaN NaN 578.0 4003.0 20785.0 13074.0 7764.0 9614 44.6 0.4539 44257 26.0 23.925698
545 Lake County OH 05000US39085 -81.392643 41.924116 228614 208258.0 9011.0 3153.0 388.0 26.0 2414.0 2083.0 9488.0 52724.0 52391.0 47296.0 18962 43.7 0.4228 61870 25.0 11.570205
546 Licking County OH 05000US39089 -82.481251 40.093609 172198 NaN NaN NaN NaN NaN NaN 719.0 8017.0 39583.0 37301.0 28906.0 21237 40.0 0.4562 58685 27.7 13.776273
547 Lorain County OH 05000US39093 -82.179722 41.438804 306365 260616.0 26803.0 2735.0 578.0 174.0 4160.0 2994.0 18362.0 63625.0 71303.0 51544.0 36299 41.3 0.4665 54504 29.7 13.838858
548 Lucas County OH 05000US39095 -83.468867 41.682321 432488 313144.0 82833.0 6710.0 816.0 233.0 10626.0 3614.0 23969.0 82241.0 102925.0 76367.0 83846 38.0 0.4933 44534 29.5 14.848766
549 Mahoning County OH 05000US39099 -80.770396 41.010880 230008 183773.0 35050.0 2048.0 402.0 0.0 1527.0 2102.0 11681.0 61280.0 46477.0 41178.0 41781 43.5 0.4745 42295 28.3 17.613765
550 Marion County OH 05000US39101 -83.172927 40.588208 65096 NaN NaN NaN NaN NaN NaN 226.0 5225.0 21017.0 13564.0 5378.0 7109 41.8 0.4307 42826 31.2 23.620291
551 Medina County OH 05000US39103 -81.899566 41.116051 177221 168281.0 2542.0 1651.0 524.0 0.0 996.0 745.0 4842.0 38245.0 38053.0 40838.0 11080 42.3 0.4221 72618 24.1 9.569103
552 Miami County OH 05000US39109 -84.228414 40.053326 104679 NaN NaN NaN NaN NaN NaN 1340.0 5397.0 26659.0 22779.0 15818.0 9827 41.7 0.3901 60170 25.1 12.995909
553 Montgomery County OH 05000US39113 -84.290545 39.755218 531239 388711.0 110639.0 11335.0 640.0 374.0 3071.0 5431.0 27512.0 101173.0 132038.0 93625.0 94861 39.3 0.4824 46936 30.2 14.987160
554 Muskingum County OH 05000US39119 -81.943506 39.966046 86068 NaN NaN NaN NaN NaN NaN 1357.0 6274.0 24485.0 18375.0 7427.0 10853 40.1 0.4240 43422 29.9 23.849103
555 Portage County OH 05000US39133 -81.196932 41.168640 161921 147855.0 6943.0 2837.0 174.0 0.0 511.0 1019.0 5375.0 37568.0 28361.0 30776.0 22213 37.4 0.4712 49695 33.9 16.187488
556 Richland County OH 05000US39139 -82.542715 40.774167 121107 104750.0 12153.0 821.0 253.0 85.0 630.0 710.0 8784.0 33975.0 25054.0 14961.0 17699 41.3 0.4246 44073 26.6 23.482049
557 Ross County OH 05000US39141 -83.059585 39.323763 77000 NaN NaN NaN NaN NaN NaN 710.0 5176.0 23946.0 15183.0 8774.0 14125 40.9 0.4579 46422 29.0 20.588645
558 Scioto County OH 05000US39145 -82.999028 38.815019 76088 NaN NaN NaN NaN NaN NaN 834.0 4837.0 22310.0 16365.0 7300.0 15073 39.7 0.4785 39210 31.3 25.270867
559 Stark County OH 05000US39151 -81.365667 40.814131 373612 327583.0 27377.0 3682.0 218.0 0.0 2474.0 3676.0 16471.0 99899.0 74191.0 62887.0 47594 41.6 0.4306 50994 26.4 15.003057
560 Summit County OH 05000US39153 -81.534936 41.121851 540300 427390.0 77916.0 17116.0 503.0 149.0 1385.0 6802.0 22042.0 119225.0 108420.0 118848.0 71951 41.1 0.4657 52036 28.4 17.357180
561 Trumbull County OH 05000US39155 -80.767656 41.308936 201825 178222.0 17537.0 860.0 364.0 0.0 291.0 1687.0 9650.0 63967.0 36585.0 28141.0 36379 44.2 0.4613 45552 29.5 20.630777
562 Tuscarawas County OH 05000US39157 -81.471157 40.447441 92420 NaN NaN NaN NaN NaN NaN 1475.0 4591.0 27681.0 15126.0 11863.0 12388 39.3 0.3900 50440 24.5 21.130748
563 Warren County OH 05000US39165 -84.169906 39.425652 227063 NaN NaN NaN NaN NaN NaN 1264.0 8084.0 39305.0 36571.0 65314.0 10777 39.0 0.4504 80207 26.1 8.061502
564 Wayne County OH 05000US39169 -81.887194 40.829661 116470 NaN NaN NaN NaN NaN NaN 852.0 5149.0 30689.0 20550.0 15730.0 13883 38.7 0.4116 53434 26.7 16.447536
565 Wood County OH 05000US39173 -83.622682 41.360183 130219 119109.0 3912.0 1938.0 510.0 234.0 1858.0 348.0 4275.0 22856.0 25074.0 27864.0 15181 34.8 0.4461 60166 26.7 12.109616
566 Canadian County OK 05000US40017 -97.979836 35.543416 136532 110859.0 3031.0 4298.0 5720.0 0.0 5825.0 1868.0 5094.0 26272.0 32066.0 23568.0 11644 35.8 0.3921 67177 27.6 15.064994
567 Cleveland County OK 05000US40027 -97.328332 35.203117 278655 218891.0 12353.0 12864.0 11785.0 142.0 3154.0 1558.0 10788.0 40921.0 62470.0 58412.0 34173 34.0 0.4259 61288 27.8 10.038819
568 Comanche County OK 05000US40031 -98.476597 34.662628 122136 76393.0 22659.0 2645.0 6613.0 225.0 2850.0 1104.0 7015.0 24924.0 26625.0 15609.0 16655 33.3 0.4348 51164 25.4 12.487787
569 Creek County OK 05000US40037 -96.379793 35.907732 71312 56935.0 1349.0 249.0 8304.0 55.0 282.0 599.0 5005.0 20216.0 13638.0 7807.0 10936 40.0 0.4342 43609 27.4 20.675813
570 Muskogee County OK 05000US40101 -95.383911 35.617551 69477 40817.0 7297.0 513.0 12438.0 103.0 1728.0 540.0 4641.0 16266.0 14042.0 9460.0 14280 37.9 0.4802 40860 31.1 21.815385
571 Oklahoma County OK 05000US40109 -97.409401 35.554611 782970 539756.0 118634.0 24246.0 24989.0 1011.0 24272.0 20703.0 39331.0 129350.0 154044.0 159102.0 125913 34.5 0.4810 51078 28.1 15.139512
572 Payne County OK 05000US40119 -96.975255 36.079225 81131 NaN NaN NaN NaN NaN NaN 649.0 2418.0 11188.0 13122.0 16090.0 20462 27.3 0.5589 35927 41.3 13.830447
573 Pottawatomie County OK 05000US40125 -96.957007 35.211393 72290 54566.0 2607.0 578.0 10306.0 60.0 368.0 355.0 4952.0 17509.0 15994.0 8057.0 12639 37.7 0.4357 41716 27.5 21.505461
574 Rogers County OK 05000US40131 -95.601337 36.378082 91766 68761.0 871.0 1044.0 10913.0 0.0 1171.0 701.0 3580.0 20763.0 21981.0 14149.0 7558 39.4 0.4135 62434 28.4 16.923528
575 Tulsa County OK 05000US40143 -95.941731 36.120120 642940 445786.0 63930.0 21241.0 32602.0 279.0 26455.0 12916.0 26963.0 109245.0 135265.0 131420.0 101057 35.5 0.4845 51325 27.9 14.113482
576 Wagoner County OK 05000US40145 -95.514100 35.963479 77679 58594.0 2716.0 951.0 6890.0 90.0 908.0 490.0 3919.0 17586.0 16697.0 13188.0 9540 39.1 0.3806 62041 25.2 10.587154
577 Benton County OR 05000US41003 -123.426317 44.490623 89385 77080.0 984.0 5884.0 459.0 262.0 1185.0 377.0 1634.0 5657.0 16729.0 29171.0 17134 32.5 0.4911 55459 41.7 6.811800
578 Clackamas County OR 05000US41005 -122.195127 45.160493 408062 357854.0 4548.0 16159.0 3639.0 626.0 8713.0 4545.0 11297.0 58999.0 106984.0 103803.0 35647 41.3 0.4488 74891 29.6 7.769196
579 Deschutes County OR 05000US41017 -121.225575 43.915118 181307 167962.0 1089.0 1730.0 806.0 87.0 3582.0 816.0 8540.0 36923.0 41362.0 43093.0 19270 41.8 0.4200 61870 30.3 11.154613
580 Douglas County OR 05000US41019 -123.154380 43.285903 108457 100124.0 642.0 1026.0 1033.0 102.0 618.0 852.0 6552.0 24933.0 31953.0 13908.0 15885 47.2 0.4685 42889 29.4 15.436579
581 Jackson County OR 05000US41029 -122.675797 42.411782 216527 192932.0 1390.0 2803.0 2298.0 635.0 8039.0 4260.0 11143.0 38812.0 57243.0 40976.0 30792 43.1 0.4666 48563 34.5 11.274425
582 Josephine County OR 05000US41033 -123.597245 42.385382 85904 NaN NaN NaN NaN NaN NaN 544.0 6905.0 17524.0 26150.0 12454.0 14826 47.1 0.4616 36472 34.3 17.161884
583 Klamath County OR 05000US41035 -121.646168 42.683761 66443 57742.0 134.0 702.0 3131.0 216.0 1793.0 2145.0 3873.0 14567.0 16319.0 8395.0 12438 42.1 0.4348 45604 29.6 21.283053
584 Lane County OR 05000US41039 -122.897678 43.928276 369519 324944.0 3663.0 9597.0 4162.0 928.0 6858.0 3970.0 16179.0 62062.0 96444.0 72815.0 68691 39.4 0.4588 47777 33.0 11.039049
585 Linn County OR 05000US41043 -122.543755 44.494824 122849 110734.0 595.0 1299.0 1248.0 74.0 4511.0 1361.0 5032.0 23747.0 35992.0 18493.0 15167 39.4 0.4535 51310 29.0 15.888091
586 Marion County OR 05000US41047 -122.576260 44.900898 336316 265422.0 4806.0 6959.0 2378.0 2402.0 23368.0 13462.0 14784.0 58814.0 79126.0 50042.0 43023 36.4 0.4143 56550 30.3 13.867126
587 Multnomah County OR 05000US41051 -122.417173 45.547693 799766 630308.0 45410.0 56252.0 5658.0 4647.0 16182.0 18592.0 27562.0 94851.0 171252.0 261887.0 113489 36.8 0.4743 62629 31.7 9.621313
588 Polk County OR 05000US41053 -123.397329 44.904395 81823 73654.0 358.0 1362.0 1905.0 369.0 1402.0 1443.0 3115.0 12616.0 19075.0 15523.0 9782 37.7 0.4222 52485 30.4 10.812195
589 Umatilla County OR 05000US41059 -118.733879 45.591200 76456 67399.0 573.0 266.0 1861.0 141.0 1998.0 2986.0 4459.0 14060.0 18499.0 7764.0 12240 35.4 0.4253 50171 28.1 18.127413
590 Washington County OR 05000US41067 -123.097615 45.553542 582779 439014.0 10846.0 58739.0 2252.0 2262.0 33702.0 12498.0 17947.0 70767.0 121926.0 170980.0 52590 36.6 0.4209 75634 28.9 6.808063
591 Yamhill County OR 05000US41071 -123.316117 45.248138 105035 91961.0 611.0 1804.0 1755.0 367.0 3317.0 1364.0 5514.0 19062.0 24713.0 18475.0 11150 38.2 0.4422 61596 27.0 14.129828
592 Adams County PA 05000US42001 -77.217730 39.869471 102180 94434.0 1057.0 426.0 65.0 0.0 3626.0 1722.0 6042.0 29115.0 17486.0 15489.0 9338 44.2 0.3985 59300 29.1 17.277377
593 Allegheny County PA 05000US42003 -79.980920 40.468920 1225365 982979.0 154858.0 43283.0 975.0 443.0 4933.0 8884.0 34875.0 241290.0 225945.0 366908.0 138887 40.6 0.4769 56140 27.2 14.349432
594 Armstrong County PA 05000US42005 -79.464128 40.812379 66486 NaN NaN NaN NaN NaN NaN 371.0 4451.0 23231.0 12443.0 7965.0 9309 46.5 0.4284 47398 28.8 23.719594
595 Beaver County PA 05000US42007 -80.350721 40.684140 167429 151718.0 9238.0 551.0 91.0 194.0 779.0 1088.0 7335.0 44875.0 37106.0 31505.0 13561 44.9 0.4167 55221 24.1 19.678540
596 Berks County PA 05000US42011 -75.926860 40.413956 414812 343554.0 18603.0 5504.0 4478.0 0.0 14887.0 8449.0 21719.0 104558.0 67703.0 70120.0 54476 40.1 0.4386 59286 30.5 14.550338
597 Blair County PA 05000US42013 -78.310640 40.497926 124650 NaN NaN NaN NaN NaN NaN 839.0 5909.0 42849.0 22536.0 16344.0 15689 43.8 0.4395 43443 27.3 22.297377
598 Bucks County PA 05000US42017 -75.107060 40.336887 626399 551410.0 22210.0 30721.0 912.0 0.0 7193.0 6081.0 19560.0 131824.0 106286.0 178760.0 40818 43.7 0.4540 79936 29.8 10.470003
599 Butler County PA 05000US42019 -79.918960 40.913834 186847 179023.0 2329.0 2478.0 177.0 0.0 727.0 1044.0 4676.0 42321.0 35499.0 48338.0 11214 43.3 0.4364 66426 25.7 12.449650
600 Cambria County PA 05000US42021 -78.715284 40.494127 134732 126107.0 3266.0 515.0 13.0 175.0 527.0 1150.0 5442.0 41827.0 25163.0 21300.0 20303 45.1 0.4288 44100 28.5 23.223429
601 Carbon County PA 05000US42025 -75.709428 40.917833 63594 NaN NaN NaN NaN NaN NaN 456.0 3805.0 21864.0 11999.0 8142.0 9152 46.2 0.4038 51676 24.3 17.097668
602 Centre County PA 05000US42027 -77.847830 40.909160 161464 142112.0 5949.0 8766.0 115.0 80.0 437.0 975.0 2969.0 29060.0 17544.0 48006.0 25619 31.7 0.4812 60266 34.9 8.895663
603 Chester County PA 05000US42029 -75.749732 39.973965 516312 440376.0 29721.0 25578.0 506.0 254.0 6783.0 6364.0 14195.0 76114.0 71295.0 178576.0 36998 40.3 0.4733 92407 28.7 9.710024
604 Clearfield County PA 05000US42033 -78.473749 41.000249 80596 NaN NaN NaN NaN NaN NaN 1072.0 5761.0 29274.0 13430.0 8363.0 9887 44.8 0.4168 47352 26.4 24.266199
605 Columbia County PA 05000US42037 -76.404260 41.045517 66420 NaN NaN NaN NaN NaN NaN 413.0 3647.0 17759.0 11288.0 10010.0 7755 40.6 0.4383 49186 26.5 19.644944
606 Crawford County PA 05000US42039 -80.107811 41.686840 86257 82613.0 1507.0 314.0 14.0 51.0 192.0 392.0 4260.0 27727.0 12691.0 13248.0 11286 42.7 0.4440 45410 22.4 19.596525
607 Cumberland County PA 05000US42041 -77.263440 40.164782 248506 218262.0 10293.0 10925.0 171.0 0.0 4149.0 1577.0 11027.0 54395.0 42258.0 62762.0 18153 40.2 0.4188 63530 25.6 13.261305
608 Dauphin County PA 05000US42043 -76.792634 40.412565 273707 193791.0 52861.0 11959.0 918.0 0.0 7929.0 2983.0 12797.0 61179.0 48024.0 61600.0 29872 39.6 0.4449 60331 27.3 13.717419
609 Delaware County PA 05000US42045 -75.398786 39.916670 563402 389846.0 120082.0 32804.0 1165.0 0.0 6352.0 5832.0 18346.0 118131.0 89724.0 146227.0 59097 39.0 0.4798 67950 30.3 11.750687
610 Erie County PA 05000US42049 -80.096386 42.117952 276207 241733.0 20144.0 4980.0 613.0 201.0 1903.0 2864.0 10557.0 77664.0 43235.0 52826.0 41799 39.2 0.4442 48964 28.1 18.505082
611 Fayette County PA 05000US42051 -79.644586 39.914115 132733 122730.0 5313.0 320.0 63.0 56.0 589.0 1096.0 9743.0 48650.0 20901.0 15813.0 22414 44.8 0.4533 43140 28.6 22.714398
612 Franklin County PA 05000US42055 -77.724485 39.926686 153851 NaN NaN NaN NaN NaN NaN 997.0 9281.0 45105.0 25800.0 23048.0 12115 41.8 0.4260 60559 25.2 19.230642
613 Indiana County PA 05000US42063 -79.087545 40.651432 86364 NaN NaN NaN NaN NaN NaN 250.0 4163.0 24710.0 13368.0 12369.0 17239 39.7 0.4606 42962 34.5 24.992813
614 Lackawanna County PA 05000US42069 -75.609587 41.440250 211321 193832.0 5870.0 5204.0 214.0 58.0 1246.0 3323.0 9262.0 55829.0 38757.0 40337.0 28922 41.7 0.4634 47475 27.6 21.166317
615 Lancaster County PA 05000US42071 -76.250198 40.041992 538500 481798.0 23427.0 10923.0 431.0 53.0 9219.0 7737.0 31564.0 132943.0 82824.0 90202.0 58032 38.2 0.4331 61335 28.3 15.485730
616 Lawrence County PA 05000US42073 -80.334446 40.992735 87294 NaN NaN NaN NaN NaN NaN 847.0 4291.0 26940.0 15194.0 14337.0 10867 45.0 0.4837 46918 28.3 22.673657
617 Lebanon County PA 05000US42075 -76.458009 40.367344 138863 120894.0 3650.0 2078.0 129.0 0.0 9250.0 1101.0 7950.0 42667.0 21504.0 19393.0 12589 41.2 0.4073 57248 27.3 15.976771
618 Lehigh County PA 05000US42077 -75.590627 40.614241 363147 286600.0 26645.0 11587.0 358.0 0.0 26967.0 6338.0 15573.0 84283.0 64008.0 72981.0 51444 39.0 0.4692 60498 31.5 14.247229
619 Luzerne County PA 05000US42079 -75.976034 41.172787 316383 285321.0 13937.0 3756.0 452.0 77.0 7116.0 3344.0 16878.0 88057.0 61987.0 52090.0 44974 43.1 0.4641 46580 27.9 22.619604
620 Lycoming County PA 05000US42081 -77.055253 41.343624 115248 NaN NaN NaN NaN NaN NaN 589.0 5310.0 30222.0 25842.0 17082.0 16487 40.6 0.4148 49052 31.4 15.883580
621 Mercer County PA 05000US42085 -80.252786 41.300014 112913 NaN NaN NaN NaN NaN NaN 960.0 5498.0 34099.0 20367.0 17607.0 17715 45.1 0.4262 49890 25.7 16.912119
622 Monroe County PA 05000US42089 -75.329037 41.056233 166098 124741.0 23137.0 3492.0 257.0 191.0 8899.0 2353.0 7623.0 40561.0 32527.0 28986.0 18575 41.9 0.4177 60095 25.7 14.812164
623 Montgomery County PA 05000US42091 -75.370201 40.209999 821725 654626.0 73076.0 60749.0 1230.0 295.0 9751.0 7024.0 22217.0 139353.0 127461.0 277024.0 49570 41.4 0.4623 84113 29.0 9.205401
624 Northampton County PA 05000US42095 -75.307447 40.752791 302294 262942.0 15889.0 8454.0 459.0 0.0 4759.0 4279.0 12630.0 74744.0 55902.0 60921.0 24583 41.6 0.4176 66438 28.2 14.046170
625 Northumberland County PA 05000US42097 -76.709877 40.851524 92541 87742.0 2471.0 432.0 127.0 0.0 665.0 1203.0 6542.0 34012.0 13107.0 11947.0 13035 44.7 0.4201 47736 23.5 26.245472
626 Philadelphia County PA 05000US42101 -75.133346 40.009375 1567872 634111.0 661032.0 110733.0 5521.0 480.0 104892.0 42857.0 115831.0 336164.0 241777.0 302678.0 391653 34.1 0.5153 41449 32.2 21.933455
627 Schuylkill County PA 05000US42107 -76.217800 40.703690 143573 133893.0 4370.0 829.0 288.0 0.0 2261.0 1711.0 9375.0 49938.0 26971.0 16565.0 17316 44.4 0.4072 50684 28.7 22.899792
628 Somerset County PA 05000US42111 -79.028486 39.981297 75061 NaN NaN NaN NaN NaN NaN 1292.0 3944.0 26815.0 13219.0 8968.0 9368 45.7 0.4300 43871 24.5 26.403782
629 Washington County PA 05000US42125 -80.252132 40.200005 207981 194397.0 6242.0 1981.0 271.0 0.0 1153.0 2047.0 7571.0 58011.0 36170.0 43932.0 18836 44.6 0.4532 57998 27.2 14.453120
630 Westmoreland County PA 05000US42129 -79.466688 40.311068 355458 336698.0 9544.0 3691.0 75.0 34.0 797.0 2414.0 10241.0 97854.0 74883.0 73462.0 33545 46.7 0.4325 56722 27.0 17.266828
631 York County PA 05000US42133 -76.728446 39.921839 443744 391834.0 25561.0 6918.0 943.0 231.0 7853.0 4262.0 22656.0 126295.0 75680.0 74615.0 43053 41.2 0.4015 62462 29.1 15.928422
632 Kent County RI 05000US44003 -71.576313 41.677750 164614 151589.0 2738.0 3675.0 196.0 128.0 1900.0 1923.0 6602.0 35352.0 37410.0 39148.0 15376 43.5 0.4716 63101 29.2 12.038909
633 Newport County RI 05000US44005 -71.284063 41.502732 82784 NaN NaN NaN NaN NaN NaN 659.0 1849.0 13102.0 16560.0 27231.0 5595 45.6 0.4561 73596 27.6 11.485832
634 Providence County RI 05000US44007 -71.578242 41.870488 633673 460377.0 58806.0 26713.0 4643.0 572.0 57811.0 22819.0 31705.0 129921.0 111879.0 127085.0 93332 37.5 0.4775 52369 29.8 17.571099
635 Washington County RI 05000US44009 -71.617612 41.401162 126288 NaN NaN NaN NaN NaN NaN 626.0 3185.0 20409.0 21610.0 40283.0 12477 45.1 0.4609 77813 30.6 10.576159
636 Aiken County SC 05000US45003 -81.633870 33.549317 167458 NaN NaN NaN NaN NaN NaN 2839.0 11573.0 38262.0 34279.0 27598.0 29696 40.8 0.4510 45945 28.3 18.946533
637 Anderson County SC 05000US45007 -82.638086 34.519549 196569 156660.0 32543.0 1935.0 309.0 0.0 1944.0 3596.0 14755.0 45300.0 39308.0 28454.0 31011 40.4 0.4554 45090 27.3 21.941616
638 Beaufort County SC 05000US45013 -80.689320 32.358147 183149 138177.0 33588.0 2367.0 659.0 106.0 4005.0 1269.0 4743.0 29958.0 39003.0 54567.0 16522 44.9 0.4731 65919 30.7 13.504681
639 Berkeley County SC 05000US45015 -79.953655 33.207700 210898 141762.0 51487.0 5728.0 644.0 0.0 5460.0 3017.0 12603.0 38064.0 48211.0 35057.0 25606 36.4 0.4266 59153 28.4 15.377251
640 Charleston County SC 05000US45019 -79.942480 32.800458 396484 271106.0 109189.0 6001.0 626.0 0.0 2356.0 4438.0 20976.0 62118.0 73572.0 117081.0 58935 37.1 0.5249 56827 31.3 14.295187
641 Darlington County SC 05000US45031 -79.962115 34.332185 67234 NaN NaN NaN NaN NaN NaN 2230.0 6718.0 15283.0 12918.0 7957.0 13028 41.2 0.4570 36841 27.7 28.896004
642 Dorchester County SC 05000US45035 -80.404697 33.082186 153773 103108.0 37763.0 2803.0 1078.0 0.0 3173.0 2483.0 8136.0 26497.0 33614.0 29784.0 12032 36.3 0.4146 57637 30.5 12.414142
643 Florence County SC 05000US45041 -79.710233 34.028535 138742 NaN NaN NaN NaN NaN NaN 3608.0 10096.0 28824.0 28327.0 20909.0 24362 39.0 0.4606 46524 28.9 26.740781
644 Greenville County SC 05000US45045 -82.372077 34.892645 498766 372089.0 91056.0 11379.0 1844.0 439.0 11693.0 8208.0 28320.0 77733.0 103316.0 115302.0 52236 38.1 0.4649 55342 29.1 16.030262
645 Greenwood County SC 05000US45047 -82.127876 34.155796 70133 NaN NaN NaN NaN NaN NaN 893.0 4751.0 13719.0 14492.0 10921.0 17166 37.8 0.4762 38200 31.1 28.351433
646 Horry County SC 05000US45051 -78.976675 33.909269 322342 260680.0 44012.0 4425.0 1727.0 192.0 6660.0 4842.0 16264.0 75308.0 84171.0 53267.0 49075 45.3 0.4465 45621 32.1 12.204329
647 Lancaster County SC 05000US45057 -80.703688 34.686818 89594 NaN NaN NaN NaN NaN NaN 373.0 6082.0 20997.0 17446.0 16927.0 11154 42.6 0.4798 56216 28.2 18.320564
648 Laurens County SC 05000US45059 -82.005657 34.483477 66777 47288.0 14837.0 981.0 128.0 471.0 1017.0 1386.0 5986.0 14229.0 15488.0 6333.0 11536 39.2 0.4245 44038 26.4 23.975005
649 Lexington County SC 05000US45063 -81.272853 33.892553 286196 227447.0 41478.0 4965.0 627.0 111.0 3819.0 4893.0 12763.0 58443.0 57904.0 60145.0 34038 38.9 0.4392 57382 30.2 14.631489
650 Oconee County SC 05000US45073 -83.061522 34.748759 76355 NaN NaN NaN NaN NaN NaN 1140.0 5210.0 16345.0 16931.0 13332.0 11691 44.7 0.4735 43743 31.4 23.169725
651 Orangeburg County SC 05000US45075 -80.802913 33.436135 87903 NaN NaN NaN NaN NaN NaN 1404.0 5864.0 18633.0 20928.0 10586.0 17341 39.4 0.5016 32450 33.3 28.207121
652 Pickens County SC 05000US45077 -82.725368 34.887361 122863 NaN NaN NaN NaN NaN NaN 2309.0 8432.0 20668.0 23150.0 21278.0 17108 36.3 0.4761 45779 27.7 18.833901
653 Richland County SC 05000US45079 -80.896566 34.029783 409549 186850.0 190887.0 10198.0 695.0 545.0 8467.0 5603.0 15745.0 56513.0 75631.0 99193.0 63266 33.4 0.4669 52030 29.6 15.514161
654 Spartanburg County SC 05000US45083 -81.991053 34.933239 301463 221226.0 61262.0 6514.0 197.0 30.0 4830.0 6293.0 20187.0 63418.0 63411.0 44975.0 47158 38.1 0.4635 47371 27.0 18.848435
655 Sumter County SC 05000US45085 -80.382472 33.916046 107396 NaN NaN NaN NaN NaN NaN 1718.0 6834.0 22046.0 24260.0 13588.0 22220 36.1 0.4923 40614 31.6 23.033857
656 York County SC 05000US45091 -81.183188 34.970190 258526 191332.0 48293.0 4854.0 2027.0 115.0 5796.0 3279.0 11294.0 44709.0 58358.0 51750.0 27864 38.7 0.4416 60767 27.0 10.918392
657 Minnehaha County SD 05000US46099 -96.795726 43.667472 187318 161240.0 9385.0 4477.0 3948.0 67.0 2887.0 2499.0 5856.0 31106.0 40397.0 40891.0 18693 34.9 0.4301 60038 26.6 10.524604
658 Pennington County SD 05000US46103 -102.823802 44.002349 109372 90775.0 1426.0 1444.0 10308.0 14.0 254.0 996.0 4693.0 19493.0 26098.0 22448.0 17125 38.3 0.4707 50950 30.2 12.042555
659 Anderson County TN 05000US47001 -84.195418 36.116731 75936 NaN NaN NaN NaN NaN NaN 1083.0 3810.0 17399.0 17801.0 12554.0 9652 43.4 0.4470 46055 25.5 15.239194
660 Blount County TN 05000US47009 -83.922973 35.688185 128670 NaN NaN NaN NaN NaN NaN 1306.0 7035.0 31089.0 29563.0 21046.0 13234 43.7 0.4504 51183 24.3 21.394347
661 Bradley County TN 05000US47011 -84.859414 35.153914 104490 NaN NaN NaN NaN NaN NaN 1328.0 6209.0 24916.0 20232.0 16509.0 13560 39.2 0.4289 44853 24.7 23.448784
662 Davidson County TN 05000US47037 -86.784790 36.169129 684410 442201.0 187329.0 25891.0 1398.0 605.0 10795.0 13024.0 38684.0 104703.0 119452.0 186581.0 98479 34.2 0.4806 54855 28.5 12.938039
663 Greene County TN 05000US47059 -82.847746 36.178998 68615 NaN NaN NaN NaN NaN NaN 1767.0 4084.0 21737.0 12545.0 8056.0 11161 44.9 0.5066 41109 32.6 25.957983
664 Hamilton County TN 05000US47065 -85.202295 35.159186 357738 271063.0 70591.0 6865.0 499.0 0.0 924.0 5625.0 18017.0 73283.0 71836.0 76657.0 45768 39.0 0.4952 47898 31.2 19.411954
665 Knox County TN 05000US47093 -83.937721 35.992727 456132 390124.0 40267.0 10650.0 1780.0 261.0 4973.0 4742.0 19300.0 73373.0 86294.0 117189.0 66094 37.3 0.4752 52102 28.7 13.874131
666 Madison County TN 05000US47113 -88.833424 35.606056 97663 NaN NaN NaN NaN NaN NaN 830.0 5564.0 26173.0 17193.0 13555.0 18688 38.0 0.4816 41791 32.1 20.116821
667 Maury County TN 05000US47119 -87.077763 35.615696 89981 NaN NaN NaN NaN NaN NaN 615.0 3621.0 21101.0 21631.0 13733.0 8894 39.9 0.4123 50591 26.7 17.872100
668 Montgomery County TN 05000US47125 -87.380887 36.500353 195734 140854.0 37819.0 5439.0 1383.0 1446.0 2057.0 1938.0 7122.0 35998.0 43735.0 31230.0 26026 30.8 0.3930 56112 28.0 12.921486
669 Putnam County TN 05000US47141 -85.496928 36.140807 75931 NaN NaN NaN NaN NaN NaN 1546.0 3834.0 18848.0 9835.0 13836.0 15077 36.7 0.5067 37437 32.0 20.958428
670 Robertson County TN 05000US47147 -86.869377 36.527530 69165 NaN NaN NaN NaN NaN NaN 1045.0 4126.0 17855.0 13342.0 9311.0 5705 37.9 0.4370 60423 26.9 20.286934
671 Rutherford County TN 05000US47149 -86.417213 35.843369 308251 239190.0 43917.0 10482.0 755.0 0.0 3974.0 2641.0 8966.0 56683.0 60483.0 61019.0 31360 33.1 0.4021 61157 28.4 10.330649
672 Sevier County TN 05000US47155 -83.521955 35.791284 96673 NaN NaN NaN NaN NaN NaN 1441.0 5759.0 26211.0 20156.0 12441.0 15180 41.3 0.4064 45609 28.2 16.554934
673 Shelby County TN 05000US47157 -89.895397 35.183794 934603 360410.0 499045.0 22771.0 2855.0 165.0 31109.0 16739.0 45725.0 156620.0 193404.0 187338.0 190483 35.5 0.5108 47690 31.7 22.925513
674 Sullivan County TN 05000US47163 -82.299397 36.510212 156667 147950.0 2912.0 664.0 184.0 418.0 1169.0 2198.0 10487.0 42430.0 32784.0 22638.0 25022 45.0 0.4491 42859 26.9 19.868817
675 Sumner County TN 05000US47165 -86.458517 36.470015 180063 160406.0 13146.0 2300.0 336.0 60.0 412.0 2119.0 6744.0 39622.0 38295.0 33146.0 17004 40.1 0.4346 60503 29.4 15.667475
676 Washington County TN 05000US47179 -82.495037 36.295665 127440 NaN NaN NaN NaN NaN NaN 1333.0 7489.0 26517.0 22159.0 27686.0 17802 39.9 0.4462 46276 27.8 11.935796
677 Williamson County TN 05000US47187 -86.896958 35.894972 219107 194634.0 9008.0 9027.0 251.0 515.0 1127.0 2869.0 5025.0 19379.0 34568.0 79559.0 12861 39.0 0.4485 106054 27.0 5.007384
678 Wilson County TN 05000US47189 -86.290210 36.148476 132781 NaN NaN NaN NaN NaN NaN 1333.0 6302.0 24004.0 31189.0 26568.0 10184 41.1 0.3937 71153 22.7 11.708974
679 Angelina County TX 05000US48005 -94.611056 31.251951 87791 NaN NaN NaN NaN NaN NaN 4296.0 7057.0 16523.0 18850.0 8976.0 16908 37.6 0.4566 41161 26.9 16.481298
680 Bastrop County TX 05000US48021 -97.311859 30.103128 82733 NaN NaN NaN NaN NaN NaN 3337.0 4847.0 14816.0 19002.0 10989.0 10251 38.7 0.4659 56508 33.4 19.114533
681 Bell County TX 05000US48027 -97.481921 31.042110 340411 217524.0 82612.0 9696.0 1945.0 2515.0 6984.0 5340.0 12673.0 52921.0 86780.0 45947.0 41475 31.0 0.4323 52275 27.4 12.921615
682 Bexar County TX 05000US48029 -98.520146 29.448671 1928680 1520769.0 151997.0 56054.0 14260.0 1282.0 129637.0 72858.0 117501.0 309505.0 361575.0 341692.0 307296 33.5 0.4595 53210 29.5 16.275257
683 Bowie County TX 05000US48037 -94.422375 33.446051 93860 65905.0 22872.0 1265.0 298.0 314.0 680.0 1001.0 5329.0 23452.0 19661.0 13412.0 17204 37.0 0.5029 45997 27.4 30.132300
684 Brazoria County TX 05000US48039 -95.434647 29.167817 354195 264794.0 45897.0 23012.0 3592.0 13.0 9487.0 7418.0 15921.0 64475.0 74223.0 63307.0 32885 36.0 0.4102 74799 24.9 11.236003
685 Brazos County TX 05000US48041 -96.302389 30.656725 220417 167258.0 22640.0 14050.0 1067.0 107.0 7067.0 6432.0 7746.0 23127.0 31998.0 45155.0 53402 26.1 0.5372 41559 40.8 10.076400
686 Cameron County TX 05000US48061 -97.478958 26.102923 422135 NaN NaN NaN NaN NaN NaN 35111.0 32579.0 66538.0 57779.0 41850.0 122269 31.5 0.4899 37061 32.6 31.026814
687 Collin County TX 05000US48085 -96.578153 33.193885 939585 661559.0 87766.0 132486.0 3583.0 822.0 23075.0 15011.0 19320.0 92547.0 163601.0 317049.0 59604 36.5 0.4236 89638 26.1 6.667981
688 Comal County TX 05000US48091 -98.255201 29.803019 134788 NaN NaN NaN NaN NaN NaN 1419.0 3524.0 24111.0 28781.0 33732.0 11575 42.6 0.4417 77425 28.2 10.307794
689 Coryell County TX 05000US48099 -97.798022 31.391177 74686 53879.0 7911.0 1656.0 960.0 558.0 788.0 2146.0 2957.0 13314.0 20447.0 7567.0 8270 31.7 0.4180 51125 26.2 11.767040
690 Dallas County TX 05000US48113 -96.778424 32.766987 2574984 1591187.0 582365.0 157592.0 6506.0 1393.0 159620.0 151883.0 159910.0 360041.0 424294.0 498808.0 414218 33.3 0.4957 54399 28.1 16.923969
691 Denton County TX 05000US48121 -97.119046 33.205005 806180 599270.0 74199.0 63864.0 3992.0 602.0 32029.0 15307.0 21312.0 92430.0 157526.0 233571.0 67887 35.1 0.4413 80613 28.4 7.592459
692 Ector County TX 05000US48135 -102.542507 31.865301 157462 134245.0 7643.0 1809.0 1069.0 0.0 8735.0 5132.0 14343.0 29241.0 27143.0 14331.0 19680 30.4 0.5128 53254 26.5 16.053086
693 Ellis County TX 05000US48139 -96.798336 32.347279 168499 141134.0 16525.0 1390.0 1104.0 83.0 2379.0 4745.0 10202.0 29355.0 38139.0 23450.0 15427 36.1 0.4152 70210 28.9 8.417587
694 El Paso County TX 05000US48141 -106.241391 31.766403 837918 674348.0 30034.0 11107.0 5360.0 299.0 95668.0 56620.0 47755.0 118897.0 161938.0 112402.0 187442 32.1 0.4598 42165 30.4 17.988152
695 Fort Bend County TX 05000US48157 -95.771015 29.526602 741237 388931.0 152115.0 146796.0 3077.0 93.0 34405.0 15009.0 23954.0 90429.0 127278.0 213489.0 63468 36.0 0.4482 90680 29.3 7.021958
696 Galveston County TX 05000US48167 -94.894865 29.228706 329431 258194.0 43453.0 11418.0 2556.0 87.0 7317.0 7357.0 16515.0 52487.0 76601.0 64075.0 41712 37.1 0.4692 64939 29.7 13.365526
697 Grayson County TX 05000US48181 -96.675699 33.624508 128235 111018.0 5527.0 1826.0 1352.0 16.0 3119.0 1875.0 6819.0 24262.0 35853.0 16843.0 15741 40.0 0.4290 52095 25.4 20.776493
698 Gregg County TX 05000US48183 -94.816276 32.486397 123745 92247.0 25793.0 1883.0 534.0 0.0 1037.0 4663.0 9549.0 22391.0 24801.0 17023.0 21654 35.5 0.4727 44219 31.0 17.244810
699 Guadalupe County TX 05000US48187 -97.949027 29.583208 155265 106494.0 11011.0 3061.0 187.0 1657.0 28146.0 3426.0 6139.0 32511.0 29021.0 27689.0 15983 37.0 0.4076 68157 27.6 12.061230
700 Harris County TX 05000US48201 -95.393037 29.857273 4589928 2880994.0 874306.0 317740.0 15178.0 3839.0 392701.0 251441.0 237148.0 686011.0 771762.0 903838.0 755013 33.3 0.4986 56377 30.1 15.416282
701 Harrison County TX 05000US48203 -94.374425 32.547993 66534 NaN NaN NaN NaN NaN NaN 1796.0 4719.0 14211.0 14596.0 7316.0 11727 37.2 0.4255 46548 29.7 30.738775
702 Hays County TX 05000US48209 -98.029267 30.061225 204470 184248.0 8627.0 3314.0 315.0 0.0 3753.0 6280.0 7686.0 26041.0 36282.0 43736.0 27807 31.2 0.4471 64658 31.1 9.008377
703 Henderson County TX 05000US48213 -95.853418 32.211633 79901 NaN NaN NaN NaN NaN NaN 2042.0 5264.0 18740.0 17519.0 10334.0 11545 43.7 0.4776 44088 24.4 22.803671
704 Hidalgo County TX 05000US48215 -98.180990 26.396384 849843 717201.0 2922.0 9403.0 1953.0 93.0 105943.0 78268.0 61965.0 110109.0 113444.0 86415.0 264099 29.2 0.4952 36176 32.3 29.879940
705 Hunt County TX 05000US48231 -96.083807 33.123438 92073 71701.0 7680.0 1488.0 1185.0 0.0 9273.0 1875.0 7375.0 17973.0 19094.0 12161.0 15098 38.1 0.4414 53962 29.1 16.174268
706 Jefferson County TX 05000US48245 -94.149331 29.854000 254679 150261.0 86060.0 10050.0 816.0 198.0 4262.0 9005.0 15139.0 56965.0 52466.0 31276.0 48241 35.9 0.4885 45390 31.1 32.916149
707 Johnson County TX 05000US48251 -97.364823 32.379511 163274 149251.0 5440.0 1305.0 1179.0 751.0 1997.0 3828.0 9733.0 40079.0 30365.0 20842.0 17098 36.9 0.4009 59895 26.7 11.861679
708 Kaufman County TX 05000US48257 -96.288378 32.598944 118350 98799.0 12733.0 1429.0 324.0 0.0 2852.0 4232.0 5371.0 27916.0 22837.0 14754.0 14678 36.0 0.4006 62033 29.0 11.750600
709 Liberty County TX 05000US48291 -94.822681 30.162188 81704 NaN NaN NaN NaN NaN NaN 3349.0 7752.0 21829.0 14329.0 4890.0 10822 36.7 0.4630 42877 27.0 23.862769
710 Lubbock County TX 05000US48303 -101.819944 33.611469 303137 248032.0 21502.0 5857.0 3901.0 1000.0 15506.0 7934.0 14974.0 44018.0 53559.0 55222.0 56791 30.6 0.4732 49136 29.3 15.691896
711 McLennan County TX 05000US48309 -97.201472 31.549493 247934 191192.0 36482.0 3858.0 1100.0 149.0 9396.0 7785.0 15462.0 43577.0 47971.0 32620.0 44677 33.3 0.4899 46860 29.9 19.466976
712 Midland County TX 05000US48329 -102.024326 31.870896 162565 NaN NaN NaN NaN NaN NaN 4869.0 9202.0 27689.0 30158.0 26830.0 13225 32.0 0.4781 65349 26.7 14.578661
713 Montgomery County TX 05000US48339 -95.503523 30.302364 556203 480382.0 30394.0 15327.0 3642.0 358.0 12616.0 12690.0 30061.0 85473.0 108245.0 120923.0 63175 37.2 0.4917 71123 26.5 9.595274
714 Nacogdoches County TX 05000US48347 -94.620250 31.620560 65806 NaN NaN NaN NaN NaN NaN 1540.0 5393.0 9178.0 10581.0 9527.0 16910 30.7 0.4954 35562 36.7 21.538128
715 Nueces County TX 05000US48355 -97.521643 27.739406 361350 326313.0 14774.0 6628.0 1487.0 211.0 5624.0 12329.0 23482.0 65369.0 77123.0 51583.0 50456 35.0 0.4400 54318 28.1 17.160011
716 Orange County TX 05000US48361 -93.893358 30.120918 84964 NaN NaN NaN NaN NaN NaN 1265.0 4433.0 22395.0 18921.0 8783.0 10662 37.4 0.4460 53480 23.5 17.567152
717 Parker County TX 05000US48367 -97.805905 32.777096 129441 121518.0 1965.0 731.0 984.0 72.0 2346.0 2716.0 6368.0 27335.0 30508.0 19066.0 11619 40.0 0.4220 66548 25.6 12.061772
718 Potter County TX 05000US48375 -101.893804 35.398675 120832 94819.0 12161.0 5672.0 302.0 202.0 2918.0 7043.0 10083.0 24537.0 22006.0 11283.0 25458 33.8 0.4804 42305 27.7 23.542521
719 Randall County TX 05000US48381 -101.895547 34.962529 132501 118130.0 3530.0 2313.0 998.0 188.0 4183.0 1519.0 6223.0 16860.0 34079.0 27418.0 11370 35.5 0.4492 67015 26.6 8.275016
720 Rockwall County TX 05000US48397 -96.407501 32.889216 93978 NaN NaN NaN NaN NaN NaN 1366.0 2431.0 11993.0 18979.0 24151.0 3646 37.5 0.3691 95731 26.8 9.108159
721 San Patricio County TX 05000US48409 -97.517165 28.011782 67655 NaN NaN NaN NaN NaN NaN 2374.0 5015.0 12709.0 14982.0 6261.0 9592 35.6 0.4279 53348 29.4 26.417238
722 Smith County TX 05000US48423 -95.269630 32.377092 225290 170784.0 39062.0 3832.0 1092.0 159.0 6557.0 8219.0 11593.0 35013.0 54675.0 36674.0 35747 36.5 0.4416 52572 31.7 19.711843
723 Tarrant County TX 05000US48439 -97.291291 32.772040 2016872 1367728.0 320375.0 109096.0 9482.0 2648.0 141326.0 69887.0 99460.0 317859.0 385894.0 395920.0 272364 34.3 0.4600 61534 28.8 11.320343
724 Taylor County TX 05000US48441 -99.893220 32.295684 136535 105853.0 10686.0 3333.0 1134.0 8.0 11670.0 2703.0 5237.0 30230.0 25366.0 18949.0 23270 32.8 0.4481 48803 29.9 23.455469
725 Tom Green County TX 05000US48451 -100.461355 31.401583 118386 105757.0 5937.0 1289.0 506.0 0.0 2978.0 2804.0 8372.0 21390.0 23587.0 18973.0 13387 33.8 0.4490 48696 29.7 22.454422
726 Travis County TX 05000US48453 -97.691270 30.239513 1199323 892987.0 99449.0 76749.0 5241.0 1443.0 81863.0 40242.0 40876.0 136023.0 203573.0 385317.0 144605 33.7 0.4810 70158 29.2 8.919202
727 Victoria County TX 05000US48469 -96.971198 28.796370 92467 NaN NaN NaN NaN NaN NaN 2730.0 5441.0 18853.0 20524.0 11553.0 12549 36.1 0.5174 53778 27.8 25.219387
728 Walker County TX 05000US48471 -95.569888 30.743090 71484 NaN NaN NaN NaN NaN NaN 1681.0 2980.0 18335.0 11933.0 9721.0 11540 34.1 0.4595 42662 33.8 11.768589
729 Webb County TX 05000US48479 -99.326641 27.770584 271193 NaN NaN NaN NaN NaN NaN 20643.0 21876.0 42525.0 31289.0 25535.0 88321 28.1 0.5040 35659 37.9 36.741831
730 Wichita County TX 05000US48485 -98.716851 33.991103 131838 104044.0 12709.0 3735.0 1428.0 151.0 3835.0 2238.0 7364.0 24748.0 27356.0 20050.0 19269 34.0 0.4503 44769 29.0 19.724771
731 Williamson County TX 05000US48491 -97.605069 30.649030 528718 421797.0 31597.0 35179.0 1678.0 143.0 15041.0 7009.0 11878.0 70903.0 114289.0 141071.0 29441 36.2 0.3942 81818 27.8 5.321011
732 Cache County UT 05000US49005 -111.744580 41.734225 122753 110095.0 968.0 3234.0 373.0 473.0 5253.0 2239.0 2911.0 11587.0 21136.0 23924.0 15452 25.3 0.4386 58003 29.5 7.744071
733 Davis County UT 05000US49011 -112.202123 41.037045 342281 304284.0 4795.0 6019.0 1841.0 2845.0 11341.0 1564.0 6085.0 42164.0 73386.0 74648.0 19800 30.9 0.3829 76905 25.2 8.553514
734 Salt Lake County UT 05000US49035 -111.924244 40.667882 1121354 882231.0 19235.0 44706.0 6570.0 16997.0 114439.0 23448.0 41677.0 159835.0 234564.0 236745.0 100511 32.7 0.4351 68665 27.9 8.746201
735 Utah County UT 05000US49049 -111.668667 40.120409 592299 548560.0 3287.0 8277.0 4182.0 5381.0 5515.0 3935.0 10613.0 47170.0 115230.0 112447.0 66456 24.6 0.4150 69799 28.2 4.965685
736 Washington County UT 05000US49053 -113.487800 37.262531 160245 143282.0 624.0 1461.0 2161.0 1646.0 7247.0 1452.0 3421.0 22390.0 44565.0 28692.0 21104 35.9 0.4430 55056 32.4 11.196740
737 Weber County UT 05000US49057 -111.875879 41.270355 247560 223579.0 2438.0 2914.0 1843.0 281.0 9278.0 3374.0 10281.0 45014.0 58359.0 33891.0 26247 32.4 0.4076 63158 25.4 11.979724
738 Albemarle County VA 05000US51003 -78.553506 38.024184 106878 86536.0 10426.0 5383.0 255.0 70.0 1618.0 1231.0 4042.0 11103.0 15694.0 37889.0 10006 38.2 0.4550 71975 28.1 11.466343
739 Arlington County VA 05000US51013 -77.100703 38.878337 230050 162749.0 19802.0 23558.0 962.0 411.0 13518.0 5284.0 3702.0 15646.0 17891.0 126910.0 18298 34.8 0.4399 110388 27.1 6.814909
740 Augusta County VA 05000US51015 -79.146682 38.167807 74997 NaN NaN NaN NaN NaN NaN 1179.0 6000.0 18869.0 14398.0 12730.0 6714 45.2 0.4082 55342 23.3 23.466154
741 Bedford County VA 05000US51019 -79.527947 37.312408 77960 NaN NaN NaN NaN NaN NaN 583.0 3634.0 17232.0 17212.0 16633.0 7653 45.8 0.4394 56479 24.4 22.968951
742 Chesterfield County VA 05000US51041 -77.585847 37.378434 339009 229400.0 76274.0 13052.0 672.0 254.0 8584.0 4418.0 9175.0 54002.0 65748.0 92193.0 23030 39.2 0.4042 76059 28.5 8.425721
743 Fairfax County VA 05000US51059 -77.276117 38.833742 1138652 705214.0 110990.0 217179.0 2448.0 979.0 53939.0 29448.0 26864.0 93196.0 144594.0 472131.0 66681 38.1 0.4260 115717 28.3 4.397668
744 Fauquier County VA 05000US51061 -77.821585 38.744103 69069 NaN NaN NaN NaN NaN NaN 511.0 2979.0 11744.0 14820.0 16483.0 3069 41.5 0.4285 94347 26.0 12.919240
745 Frederick County VA 05000US51069 -78.263916 39.203637 84421 NaN NaN NaN NaN NaN NaN 1026.0 3574.0 19234.0 16832.0 16515.0 3717 40.9 0.3798 69827 30.6 13.237971
746 Hanover County VA 05000US51085 -77.490992 37.760165 104392 90528.0 8977.0 1023.0 281.0 156.0 235.0 731.0 4351.0 17270.0 19484.0 29212.0 6064 44.1 0.4050 83135 27.0 11.560465
747 Henrico County VA 05000US51087 -77.300333 37.437521 326501 188411.0 95882.0 27058.0 591.0 283.0 3972.0 3064.0 12625.0 49896.0 62947.0 94750.0 29593 38.8 0.4716 66337 28.8 13.298679
748 James City County VA 05000US51095 -76.778319 37.324427 74404 NaN NaN NaN NaN NaN NaN 391.0 1882.0 9119.0 15025.0 27021.0 3758 47.2 0.4628 83455 32.1 8.606039
749 Loudoun County VA 05000US51107 -77.638857 39.081130 385945 258094.0 27802.0 67889.0 1213.0 769.0 11962.0 8900.0 7622.0 31121.0 51199.0 147019.0 13850 35.9 0.3700 134464 26.7 3.812962
750 Montgomery County VA 05000US51121 -80.387314 37.174884 98602 84345.0 4420.0 5442.0 419.0 30.0 974.0 642.0 2093.0 12037.0 12015.0 27611.0 19715 28.2 0.5297 55706 31.9 7.567538
751 Prince William County VA 05000US51153 -77.478887 38.702332 455210 254644.0 93401.0 40673.0 1594.0 356.0 41139.0 15463.0 16532.0 61094.0 75872.0 117211.0 36462 34.9 0.3888 97986 31.1 5.030331
752 Roanoke County VA 05000US51161 -80.190110 37.331077 94031 82416.0 5908.0 2895.0 232.0 0.0 1166.0 1021.0 3668.0 16022.0 23310.0 22451.0 7242 43.5 0.4540 60454 24.7 15.819517
753 Rockingham County VA 05000US51165 -78.876307 38.511257 79744 NaN NaN NaN NaN NaN NaN 1173.0 5149.0 18862.0 12198.0 14586.0 7805 41.8 0.4467 57755 26.4 19.191304
754 Spotsylvania County VA 05000US51177 -77.656280 38.182311 132010 94731.0 21641.0 3101.0 164.0 49.0 7674.0 2420.0 5072.0 26384.0 25825.0 26472.0 9409 39.0 0.3565 81146 31.5 8.189001
755 Stafford County VA 05000US51179 -77.459043 38.418933 144361 96123.0 25628.0 5862.0 666.0 90.0 8659.0 1272.0 4754.0 20290.0 29233.0 36275.0 7905 35.9 0.4034 97484 31.3 4.583449
756 York County VA 05000US51199 -76.395533 37.220914 67976 50925.0 8714.0 4245.0 241.0 612.0 566.0 874.0 1203.0 9490.0 12358.0 20903.0 2623 39.5 0.3822 89418 28.0 6.051183
757 Alexandria city VA 05000US51510 -77.082026 38.818343 155810 92717.0 34500.0 9726.0 99.0 146.0 11250.0 5011.0 4248.0 12739.0 21383.0 73206.0 19256 36.6 0.4621 87920 28.9 6.691950
758 Chesapeake city VA 05000US51550 -76.301788 36.679376 237940 147623.0 71159.0 7197.0 68.0 0.0 2808.0 2991.0 10013.0 41274.0 54376.0 50669.0 18606 36.9 0.4120 72928 30.9 9.013483
759 Hampton city VA 05000US51650 -76.297149 37.048030 135410 56219.0 68730.0 3506.0 75.0 476.0 1902.0 1348.0 5919.0 26995.0 31164.0 24831.0 22696 36.2 0.4407 50435 30.0 12.426866
760 Lynchburg city VA 05000US51680 -79.195458 37.399016 80212 53004.0 23624.0 795.0 312.0 0.0 235.0 868.0 3665.0 11592.0 12204.0 14714.0 10175 27.7 0.4848 41264 28.6 20.333999
761 Newport News city VA 05000US51700 -76.521719 37.075978 181825 86739.0 73750.0 4472.0 430.0 202.0 3401.0 1231.0 8391.0 31020.0 42624.0 31728.0 23008 33.4 0.4361 50524 33.1 13.016798
762 Norfolk city VA 05000US51710 -76.244641 36.923015 245115 115675.0 100685.0 9313.0 832.0 496.0 8111.0 2760.0 12610.0 38949.0 52402.0 42731.0 43790 30.6 0.4855 46467 32.5 12.875668
763 Portsmouth city VA 05000US51740 -76.356269 36.859430 95252 38231.0 49467.0 1626.0 265.0 1001.0 1566.0 1390.0 5615.0 18851.0 22274.0 13217.0 15487 35.0 0.4236 48516 36.5 17.025809
764 Richmond city VA 05000US51760 -77.476008 37.531399 223170 101987.0 105155.0 4903.0 650.0 115.0 1948.0 4139.0 14529.0 33617.0 39174.0 59674.0 56983 33.4 0.5550 42373 35.1 22.989183
765 Roanoke city VA 05000US51770 -79.958472 37.277830 99660 NaN NaN NaN NaN NaN NaN 1254.0 6779.0 24672.0 21515.0 14711.0 23965 38.7 0.4621 37044 32.4 21.706583
766 Suffolk city VA 05000US51800 -76.634781 36.697157 89273 45912.0 37324.0 1688.0 191.0 89.0 583.0 979.0 4270.0 17173.0 20929.0 15757.0 10024 38.2 0.4355 66669 32.8 15.432783
767 Virginia Beach city VA 05000US51810 -76.024020 36.779322 452602 303452.0 82660.0 30196.0 901.0 175.0 9160.0 3413.0 14265.0 65040.0 114133.0 107278.0 34792 35.8 0.4180 71117 29.9 8.198214
768 Chittenden County VT 05000US50007 -73.070525 44.460676 161531 145945.0 4444.0 5969.0 481.0 44.0 1023.0 1352.0 2924.0 21528.0 25888.0 53549.0 15818 37.2 0.4477 68843 31.9 8.594509
769 Benton County WA 05000US53005 -119.516864 46.228072 193686 161191.0 3631.0 4742.0 1070.0 81.0 16728.0 5501.0 4794.0 29319.0 46846.0 37187.0 18867 35.6 0.4243 62508 27.2 8.713122
770 Chelan County WA 05000US53007 -120.618543 47.859891 76338 NaN NaN NaN NaN NaN NaN 3165.0 5245.0 13673.0 14361.0 13546.0 6174 40.3 0.4566 52080 24.5 20.900787
771 Clallam County WA 05000US53009 -123.930611 48.113009 74570 65652.0 883.0 774.0 3385.0 48.0 728.0 365.0 4650.0 16323.0 20768.0 13464.0 11565 50.8 0.4144 48587 28.6 14.860068
772 Clark County WA 05000US53011 -122.485903 45.771674 467018 394477.0 8011.0 22913.0 2095.0 3821.0 14914.0 5029.0 16936.0 75961.0 121443.0 92706.0 40514 38.0 0.4269 69062 28.9 7.686018
773 Cowlitz County WA 05000US53015 -122.658682 46.185923 105160 95117.0 403.0 926.0 1316.0 69.0 1800.0 1215.0 6460.0 22978.0 28857.0 12522.0 17297 42.4 0.4521 50637 31.6 14.702514
774 Franklin County WA 05000US53021 -118.906944 46.534580 90160 54718.0 1733.0 1777.0 726.0 759.0 26894.0 6511.0 4749.0 12158.0 17898.0 8272.0 14024 29.3 0.4107 57670 29.3 13.044821
775 Grant County WA 05000US53025 -119.467788 47.213633 93546 59251.0 558.0 773.0 843.0 0.0 26679.0 4197.0 4928.0 14719.0 18172.0 10194.0 15671 32.9 0.4275 48335 23.8 16.094670
776 Grays Harbor County WA 05000US53027 -123.827043 47.142786 71628 62373.0 1187.0 1304.0 2996.0 132.0 514.0 1809.0 4237.0 18248.0 18355.0 8684.0 8630 43.9 0.4564 49623 29.6 11.961215
777 Island County WA 05000US53029 -122.670503 48.158436 82636 70785.0 1993.0 3901.0 898.0 318.0 914.0 562.0 1956.0 14168.0 24536.0 18813.0 8590 45.4 0.4359 64813 27.6 6.638693
778 King County WA 05000US53033 -121.832375 47.493554 2149970 1393480.0 130762.0 361162.0 11288.0 17123.0 97649.0 42414.0 62934.0 230070.0 393756.0 784298.0 196355 37.1 0.4693 86095 27.9 6.319427
779 Kitsap County WA 05000US53035 -122.649636 47.639687 264811 213321.0 6587.0 11318.0 3022.0 2204.0 6781.0 2618.0 7864.0 41891.0 71354.0 58621.0 25833 38.5 0.4233 69171 26.7 8.445512
780 Lewis County WA 05000US53041 -122.377444 46.580071 77066 69504.0 542.0 932.0 1279.0 26.0 1836.0 1429.0 3770.0 16293.0 22850.0 9063.0 10097 43.7 0.4140 45523 34.5 16.120506
781 Pierce County WA 05000US53053 -122.144709 47.040716 861312 629794.0 55912.0 52850.0 10825.0 12192.0 28744.0 12416.0 34003.0 155404.0 213234.0 155159.0 102454 36.1 0.4356 64434 30.1 10.489154
782 Skagit County WA 05000US53057 -121.816278 48.493066 123681 102076.0 736.0 2563.0 2356.0 354.0 11035.0 2894.0 5001.0 22387.0 33643.0 20920.0 13339 42.0 0.4279 60983 28.4 8.911531
783 Snohomish County WA 05000US53061 -121.766412 48.054913 787620 598184.0 24290.0 80706.0 7930.0 4176.0 27135.0 9618.0 28077.0 132054.0 198041.0 170822.0 61249 38.0 0.4074 78716 30.0 7.302632
784 Spokane County WA 05000US53063 -117.404392 47.620379 499072 437088.0 9113.0 11616.0 8027.0 2794.0 9051.0 3350.0 14139.0 79505.0 135148.0 104699.0 63748 37.3 0.4535 53043 30.0 10.476132
785 Thurston County WA 05000US53067 -122.829441 46.932598 275222 226065.0 8506.0 16030.0 3433.0 2555.0 2870.0 2907.0 7961.0 40089.0 73967.0 68492.0 28522 39.5 0.4119 65783 33.3 9.578417
786 Whatcom County WA 05000US53073 -121.836433 48.842653 216800 178402.0 2619.0 9298.0 6037.0 524.0 11147.0 3479.0 7136.0 35712.0 47627.0 46819.0 33560 37.0 0.4351 56411 32.2 9.871326
787 Yakima County WA 05000US53077 -120.740145 46.456558 249636 194416.0 3499.0 2502.0 10785.0 35.0 30169.0 16206.0 16593.0 43273.0 45612.0 24032.0 44367 32.8 0.4486 48965 27.6 19.457600
788 Brown County WI 05000US55009 -87.995926 44.473961 260401 221450.0 5768.0 7972.0 7072.0 22.0 8699.0 3177.0 6941.0 55002.0 57656.0 49247.0 24823 37.1 0.4367 57783 24.5 14.538567
789 Dane County WI 05000US55025 -89.417852 43.067468 531273 443862.0 25227.0 30922.0 2137.0 243.0 11260.0 3904.0 7464.0 61626.0 92168.0 180349.0 58325 35.0 0.4402 70796 28.6 7.865530
790 Dodge County WI 05000US55027 -88.704379 43.422706 88068 85269.0 1584.0 107.0 105.0 0.0 149.0 922.0 4325.0 25133.0 21761.0 10395.0 8242 42.1 0.3951 55856 25.1 17.533480
791 Eau Claire County WI 05000US55035 -91.286414 44.726355 102965 NaN NaN NaN NaN NaN NaN 996.0 2701.0 16107.0 23900.0 19697.0 13273 34.2 0.4524 49821 30.0 13.293958
792 Fond du Lac County WI 05000US55039 -88.493284 43.754722 102144 94027.0 762.0 1494.0 528.0 25.0 3086.0 1363.0 4110.0 26412.0 21726.0 16894.0 6554 41.0 0.3974 58310 27.7 15.731961
793 Jefferson County WI 05000US55055 -88.773985 43.013807 84625 NaN NaN NaN NaN NaN NaN 1583.0 3612.0 18712.0 19172.0 14083.0 8002 39.6 0.3854 58703 28.6 16.041757
794 Kenosha County WI 05000US55059 -87.424898 42.579703 168183 143113.0 12119.0 2482.0 827.0 175.0 3251.0 2685.0 9477.0 33687.0 36205.0 27922.0 22835 39.0 0.4409 59417 28.7 15.200509
795 La Crosse County WI 05000US55063 -91.111758 43.908222 118122 107056.0 1327.0 5207.0 217.0 9.0 1091.0 379.0 2785.0 16437.0 27267.0 27709.0 15979 35.5 0.4375 54823 29.9 12.955018
796 Manitowoc County WI 05000US55071 -87.313828 44.105108 79536 74773.0 485.0 2022.0 365.0 0.0 217.0 1197.0 3164.0 24440.0 16900.0 10924.0 6928 44.6 0.3868 51752 22.7 18.309283
797 Marathon County WI 05000US55073 -89.757823 44.898036 135603 123417.0 483.0 7976.0 456.0 0.0 760.0 1748.0 5219.0 32944.0 29059.0 23013.0 14934 40.8 0.4412 54774 23.6 15.839484
798 Milwaukee County WI 05000US55079 -87.481575 43.017655 951448 562559.0 249436.0 40031.0 4836.0 168.0 57968.0 18884.0 48539.0 178628.0 181332.0 191805.0 181954 34.7 0.4870 47607 30.0 18.257727
799 Outagamie County WI 05000US55087 -88.464988 44.418226 184526 165748.0 2474.0 6610.0 2854.0 65.0 3225.0 2075.0 4831.0 39193.0 43459.0 33885.0 15680 38.2 0.4173 61149 24.5 12.748993
800 Ozaukee County WI 05000US55089 -87.496553 43.360715 88314 NaN NaN NaN NaN NaN NaN 190.0 1689.0 13114.0 16375.0 29372.0 5667 44.0 0.4987 84415 27.1 10.667194
801 Portage County WI 05000US55097 -89.498070 44.476246 70447 66177.0 485.0 1476.0 162.0 0.0 508.0 313.0 1808.0 13982.0 14493.0 14245.0 7926 36.6 0.4258 53655 27.5 13.642044
802 Racine County WI 05000US55101 -87.414676 42.754075 195140 156136.0 21484.0 2903.0 1268.0 0.0 7096.0 2584.0 9481.0 46963.0 41936.0 30594.0 26423 40.0 0.4328 55706 28.6 13.693181
803 Rock County WI 05000US55105 -89.075119 42.669931 161620 140747.0 7575.0 1734.0 563.0 0.0 6456.0 2024.0 7586.0 37499.0 37683.0 23697.0 21082 40.1 0.4274 50729 28.8 17.563506
804 St. Croix County WI 05000US55109 -92.447284 45.028959 88029 84757.0 746.0 769.0 297.0 6.0 143.0 300.0 950.0 13731.0 22590.0 21288.0 5623 38.3 0.4264 72865 23.8 14.426181
805 Sheboygan County WI 05000US55117 -87.730545 43.746001 115427 104025.0 2495.0 6384.0 291.0 0.0 251.0 1129.0 3466.0 29073.0 25697.0 20649.0 6094 41.5 0.4102 54059 23.0 16.641484
806 Walworth County WI 05000US55127 -88.541731 42.668109 102959 96375.0 1042.0 1026.0 90.0 0.0 2260.0 881.0 3450.0 21587.0 21081.0 19720.0 11176 39.9 0.4511 58302 32.0 15.180199
807 Washington County WI 05000US55131 -88.232917 43.391156 134296 NaN NaN NaN NaN NaN NaN 375.0 3703.0 27883.0 32721.0 28882.0 7163 42.9 0.4380 73502 25.8 12.062535
808 Waukesha County WI 05000US55133 -88.306707 43.019308 398424 367790.0 6221.0 13818.0 1459.0 156.0 3701.0 1559.0 7902.0 66421.0 82677.0 120152.0 20268 43.2 0.4535 81878 26.7 9.374261
809 Winnebago County WI 05000US55139 -88.668149 44.085707 169886 156755.0 3786.0 4567.0 1004.0 0.0 1112.0 1160.0 6549.0 38900.0 34015.0 32405.0 19587 38.6 0.4468 56754 26.8 12.467295
810 Wood County WI 05000US55141 -90.038825 44.461413 73107 NaN NaN NaN NaN NaN NaN 718.0 2920.0 19447.0 17370.0 11131.0 6315 44.1 0.4425 51887 23.2 18.513542
811 Berkeley County WV 05000US54003 -78.032338 39.457854 113525 NaN NaN NaN NaN NaN NaN 469.0 8584.0 29594.0 22489.0 16244.0 15881 38.2 0.4266 57357 28.1 11.213387
812 Cabell County WV 05000US54011 -82.243392 38.419580 95987 87445.0 4977.0 1307.0 15.0 0.0 149.0 1035.0 5584.0 19920.0 18593.0 16147.0 21347 38.0 0.5100 38823 31.5 20.924213
813 Harrison County WV 05000US54033 -80.386487 39.279199 68400 NaN NaN NaN NaN NaN NaN 632.0 3851.0 18635.0 12263.0 12260.0 8853 41.8 0.4590 47204 27.6 16.470207
814 Kanawha County WV 05000US54039 -81.523522 38.328061 186241 164796.0 13019.0 2148.0 439.0 0.0 194.0 1898.0 12677.0 48425.0 35328.0 34075.0 31626 43.4 0.4983 45001 29.6 19.466407
815 Monongalia County WV 05000US54061 -80.059074 39.633645 104622 NaN NaN NaN NaN NaN NaN 547.0 4002.0 19932.0 13517.0 26114.0 20350 31.4 0.5439 50953 32.0 12.628233
816 Raleigh County WV 05000US54081 -81.264671 37.762470 76601 NaN NaN NaN NaN NaN NaN 599.0 4322.0 22513.0 15679.0 10792.0 10909 41.2 0.4284 45863 29.8 21.129769
817 Wood County WV 05000US54107 -81.515928 39.211679 85643 NaN NaN NaN NaN NaN NaN 292.0 3520.0 19741.0 20671.0 15349.0 15465 43.0 0.4738 48655 30.1 16.747164
818 Laramie County WY 05000US56021 -104.660395 41.292830 98136 88958.0 2690.0 1200.0 443.0 362.0 1209.0 621.0 2900.0 17034.0 27792.0 18349.0 9270 36.4 0.3927 62221 28.7 11.497865
819 Natrona County WY 05000US56025 -106.764877 42.973641 81039 NaN NaN NaN NaN NaN NaN 209.0 2832.0 18305.0 19822.0 12691.0 7256 36.0 0.4335 59474 29.0 12.860663
In [3]:
#df_transform = pd.DataFrame(pd.to_numeric(df.percent_no_internet, df.GEOID, df.P_total, df.P_white, df.P_black, df.P_asian, df.P_native, df.P_hawaiian, df.P_others, df.P_below_middle_school, df. P_some_high_school, df.P_some_high_school))
In [4]:
df_transform = pd.read_csv('~/Desktop/Python Exercises/kaggle_internet.csv')
pd.set_option('display.max_rows', 820)
pd.set_option('display.max_columns', 23)
df_transform.head(5)
Out[4]:
county state GEOID lon lat P_total P_white P_black P_asian P_native P_hawaiian P_others P_below_middle_school P_some_high_school P_high_school_equivalent P_some_college P_bachelor_and_above P_below_poverty median_age gini_index median_household_income median_rent_per_income percent_no_internet
0 Anchorage Municipality AK 05000US02020 -149.274354 61.177549 298192 184841.0 16102.0 27142.0 23916.0 7669.0 7935.0 2234.0 8196.0 44804.0 66162.0 70713.0 18302 33.0 0.4018 85634 28.0 6.593887
1 Fairbanks North Star Borough AK 05000US02090 -146.599867 64.690832 100605 75501.0 4385.0 3875.0 7427.0 503.0 2357.0 924.0 1527.0 14725.0 24570.0 19257.0 9580 30.6 0.3756 77328 25.6 12.102458
2 Matanuska-Susitna Borough AK 05000US02170 -149.407974 62.182173 104365 86314.0 1019.0 1083.0 5455.0 141.0 325.0 337.0 2755.0 21071.0 28472.0 12841.0 9893 34.2 0.4351 69332 29.6 11.156575
3 Baldwin County AL 05000US01003 -87.746067 30.659218 208563 180484.0 18821.0 914.0 1383.0 0.0 1469.0 3245.0 10506.0 41822.0 46790.0 43547.0 23375 42.4 0.4498 56732 29.3 17.868167
4 Calhoun County AL 05000US01015 -85.822513 33.771706 114611 NaN NaN NaN NaN NaN NaN 2455.0 8853.0 24761.0 26625.0 12909.0 18193 39.1 0.4692 41687 24.8 23.464932
In [5]:
print(df_transform.dtypes)
county                       object
state                        object
GEOID                        object
lon                         float64
lat                         float64
P_total                       int64
P_white                     float64
P_black                     float64
P_asian                     float64
P_native                    float64
P_hawaiian                  float64
P_others                    float64
P_below_middle_school       float64
P_some_high_school          float64
P_high_school_equivalent    float64
P_some_college              float64
P_bachelor_and_above        float64
P_below_poverty               int64
median_age                  float64
gini_index                  float64
median_household_income       int64
median_rent_per_income      float64
percent_no_internet         float64
dtype: object
In [6]:
cols = ['P_total', 'P_white', 'P_black', 'P_asian', 'P_native', 'P_hawaiian', 'P_others', 'P_below_middle_school', 'P_some_high_school', 'P_high_school_equivalent', 'P_some_college', 'P_bachelor_and_above', 'P_below_poverty', 'median_age', 'median_household_income', 'median_rent_per_income','percent_no_internet']
df_transform[cols] = df_transform[cols].apply(pd.to_numeric, errors='coerce', axis=1)
In [7]:
df_transform.head(5)
Out[7]:
county state GEOID lon lat P_total P_white P_black P_asian P_native P_hawaiian P_others P_below_middle_school P_some_high_school P_high_school_equivalent P_some_college P_bachelor_and_above P_below_poverty median_age gini_index median_household_income median_rent_per_income percent_no_internet
0 Anchorage Municipality AK 05000US02020 -149.274354 61.177549 298192.0 184841.0 16102.0 27142.0 23916.0 7669.0 7935.0 2234.0 8196.0 44804.0 66162.0 70713.0 18302.0 33.0 0.4018 85634.0 28.0 6.593887
1 Fairbanks North Star Borough AK 05000US02090 -146.599867 64.690832 100605.0 75501.0 4385.0 3875.0 7427.0 503.0 2357.0 924.0 1527.0 14725.0 24570.0 19257.0 9580.0 30.6 0.3756 77328.0 25.6 12.102458
2 Matanuska-Susitna Borough AK 05000US02170 -149.407974 62.182173 104365.0 86314.0 1019.0 1083.0 5455.0 141.0 325.0 337.0 2755.0 21071.0 28472.0 12841.0 9893.0 34.2 0.4351 69332.0 29.6 11.156575
3 Baldwin County AL 05000US01003 -87.746067 30.659218 208563.0 180484.0 18821.0 914.0 1383.0 0.0 1469.0 3245.0 10506.0 41822.0 46790.0 43547.0 23375.0 42.4 0.4498 56732.0 29.3 17.868167
4 Calhoun County AL 05000US01015 -85.822513 33.771706 114611.0 NaN NaN NaN NaN NaN NaN 2455.0 8853.0 24761.0 26625.0 12909.0 18193.0 39.1 0.4692 41687.0 24.8 23.464932
In [8]:
print(df_transform.dtypes)
county                       object
state                        object
GEOID                        object
lon                         float64
lat                         float64
P_total                     float64
P_white                     float64
P_black                     float64
P_asian                     float64
P_native                    float64
P_hawaiian                  float64
P_others                    float64
P_below_middle_school       float64
P_some_high_school          float64
P_high_school_equivalent    float64
P_some_college              float64
P_bachelor_and_above        float64
P_below_poverty             float64
median_age                  float64
gini_index                  float64
median_household_income     float64
median_rent_per_income      float64
percent_no_internet         float64
dtype: object
In [9]:
#create new column with GEOID manipulated to become FIPS codes for chloropleth
def func(row):
    
    return row.GEOID[-5:]

df_transform['FIPS'] = df.apply(func,axis=1)
pd.set_option('display.max_columns', 24)
In [10]:
df_transform.head(5)
Out[10]:
county state GEOID lon lat P_total P_white P_black P_asian P_native P_hawaiian P_others P_below_middle_school P_some_high_school P_high_school_equivalent P_some_college P_bachelor_and_above P_below_poverty median_age gini_index median_household_income median_rent_per_income percent_no_internet FIPS
0 Anchorage Municipality AK 05000US02020 -149.274354 61.177549 298192.0 184841.0 16102.0 27142.0 23916.0 7669.0 7935.0 2234.0 8196.0 44804.0 66162.0 70713.0 18302.0 33.0 0.4018 85634.0 28.0 6.593887 02020
1 Fairbanks North Star Borough AK 05000US02090 -146.599867 64.690832 100605.0 75501.0 4385.0 3875.0 7427.0 503.0 2357.0 924.0 1527.0 14725.0 24570.0 19257.0 9580.0 30.6 0.3756 77328.0 25.6 12.102458 02090
2 Matanuska-Susitna Borough AK 05000US02170 -149.407974 62.182173 104365.0 86314.0 1019.0 1083.0 5455.0 141.0 325.0 337.0 2755.0 21071.0 28472.0 12841.0 9893.0 34.2 0.4351 69332.0 29.6 11.156575 02170
3 Baldwin County AL 05000US01003 -87.746067 30.659218 208563.0 180484.0 18821.0 914.0 1383.0 0.0 1469.0 3245.0 10506.0 41822.0 46790.0 43547.0 23375.0 42.4 0.4498 56732.0 29.3 17.868167 01003
4 Calhoun County AL 05000US01015 -85.822513 33.771706 114611.0 NaN NaN NaN NaN NaN NaN 2455.0 8853.0 24761.0 26625.0 12909.0 18193.0 39.1 0.4692 41687.0 24.8 23.464932 01015
In [11]:
print(df_transform.dtypes)
county                       object
state                        object
GEOID                        object
lon                         float64
lat                         float64
P_total                     float64
P_white                     float64
P_black                     float64
P_asian                     float64
P_native                    float64
P_hawaiian                  float64
P_others                    float64
P_below_middle_school       float64
P_some_high_school          float64
P_high_school_equivalent    float64
P_some_college              float64
P_bachelor_and_above        float64
P_below_poverty             float64
median_age                  float64
gini_index                  float64
median_household_income     float64
median_rent_per_income      float64
percent_no_internet         float64
FIPS                         object
dtype: object
In [10]:
df_transform['FIPS'] = df_transform['FIPS'].apply(lambda x: '{0:0>5}'.format(x))
df_transform.head(5)
Out[10]:
county state GEOID lon lat P_total P_white P_black P_asian P_native P_hawaiian P_others P_below_middle_school P_some_high_school P_high_school_equivalent P_some_college P_bachelor_and_above P_below_poverty median_age gini_index median_household_income median_rent_per_income percent_no_internet FIPS
0 Anchorage Municipality AK 05000US02020 -149.274354 61.177549 298192.0 184841.0 16102.0 27142.0 23916.0 7669.0 7935.0 2234.0 8196.0 44804.0 66162.0 70713.0 18302.0 33.0 0.4018 85634.0 28.0 6.593887 02020
1 Fairbanks North Star Borough AK 05000US02090 -146.599867 64.690832 100605.0 75501.0 4385.0 3875.0 7427.0 503.0 2357.0 924.0 1527.0 14725.0 24570.0 19257.0 9580.0 30.6 0.3756 77328.0 25.6 12.102458 02090
2 Matanuska-Susitna Borough AK 05000US02170 -149.407974 62.182173 104365.0 86314.0 1019.0 1083.0 5455.0 141.0 325.0 337.0 2755.0 21071.0 28472.0 12841.0 9893.0 34.2 0.4351 69332.0 29.6 11.156575 02170
3 Baldwin County AL 05000US01003 -87.746067 30.659218 208563.0 180484.0 18821.0 914.0 1383.0 0.0 1469.0 3245.0 10506.0 41822.0 46790.0 43547.0 23375.0 42.4 0.4498 56732.0 29.3 17.868167 01003
4 Calhoun County AL 05000US01015 -85.822513 33.771706 114611.0 NaN NaN NaN NaN NaN NaN 2455.0 8853.0 24761.0 26625.0 12909.0 18193.0 39.1 0.4692 41687.0 24.8 23.464932 01015
In [13]:
print(df_transform.dtypes)
county                       object
state                        object
GEOID                        object
lon                         float64
lat                         float64
P_total                     float64
P_white                     float64
P_black                     float64
P_asian                     float64
P_native                    float64
P_hawaiian                  float64
P_others                    float64
P_below_middle_school       float64
P_some_high_school          float64
P_high_school_equivalent    float64
P_some_college              float64
P_bachelor_and_above        float64
P_below_poverty             float64
median_age                  float64
gini_index                  float64
median_household_income     float64
median_rent_per_income      float64
percent_no_internet         float64
FIPS                         object
dtype: object

Now that the data is cleaned up, I wanted to take a quick look at internet access throughout the country. I thought this may be a way to assess my question regarding internet deserts.

In [14]:
#county chloropleth map of households without internet
colorscale = ["#f7fbff","#ebf3fb","#deebf7","#d2e3f3","#c6dbef","#b3d2e9","#9ecae1",
              "#85bcdb","#6baed6","#57a0ce","#4292c6","#3082be","#2171b5","#1361a9",
              "#08519c","#0b4083","#08306b"]
endpts = list(np.linspace(0, 55, len(colorscale) - 1))
fips = df_transform['FIPS'].tolist()
values = df_transform['percent_no_internet'].tolist()

fig = ff.create_choropleth(
    fips=fips, values=values,
    binning_endpoints=endpts,
    colorscale=colorscale,
    show_state_data=False,
    show_hover=True, centroid_marker={'opacity': 0},
    asp=2.9, title='Percentage of People with No Internet',
    legend_title='% without internet'
    
)
iplot(fig, filename='choropleth_full_usa')

I used a Chloropleth map here to gain a quick visual, and confirm or deny my initial thoughts about the dataset. This guided my code, because as you’ll see above there are some counties with very dark blue sections to indicate lack of internet access. I was immediately intrigued by the situation in Apache County, Arizona with 54% of people without internet. It also shows how much of the data was excluded or unavailable. This may make the data we have helpful if there are strong correlations between the datapoints in it.

In [15]:
#state cholorpleth of households without internet
import plotly.plotly as py
import pandas as pd

df_transform 

for col in df.columns:
    df[col] = df[col].astype(str)

scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\
            [0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]

df['text'] = df['state'] + '<br>' +\
    'Median Household Income '+df['median_household_income']+' Median Age '+df['median_age']+'<br>'+\
    'Below Poverty '+df['P_below_poverty']+' Without Internet ' + df['percent_no_internet']

data = [ dict(
        type='choropleth',
        colorscale = scl,
        autocolorscale = False,
        locations = df['state'],
        z = df['percent_no_internet'].astype(float),
        locationmode = 'USA-states',
        text = df['text'],
        marker = dict(
            line = dict (
                color = 'rgb(255,255,255)',
                width = 2
            ) ),
        colorbar = dict(
            title = "% of Homes Without Internet")
        ) ]

layout = dict(
        title = 'Homes Without Internet by State',
        geo = dict(
            scope='usa',
            projection=dict( type='albers usa' ),
            showlakes = True,
            lakecolor = 'rgb(255, 255, 255)'),
             )
    
fig = dict( data=data, layout=layout )
iplot( fig, filename='d3-cloropleth-map' )

I also used a state chloropleth map to see if there were entire states that might have less access to internet, and begin to get a good idea of where “internet deserts” might be in the US. As you see here, the South, and Midwestern United States have many populations that do not have access to internet.

In [16]:
#for reference I've calculated the mean, and standard deviations, so we can learn what is statistically significant.
#this still includes outliers like Apache County, AZ
mean = df_transform.percent_no_internet.mean()
print('Statistics for Percent of Household Without Internet Access:')
print('Mean:')
print(mean)
variance = df_transform.percent_no_internet.var()
print('Variance:')
print(variance)
std_dev = df_transform.percent_no_internet.std()
print('Standard Deviation:')
print(std_dev)
one_above = (mean + std_dev)
print('One Standard Deviation Above the Mean:')
print(one_above)
three_above = (mean + (std_dev * 3))
print('Three Standard Deviations Above the Mean')
print(three_above)
one_below = (mean - std_dev)
print('One Standard Deviation Below the Mean:')
print(one_below)
three_below = (mean - (std_dev * 3))
print('Three Standard Deviations Below the Mean:')
print(three_below)
Statistics for Percent of Household Without Internet Access:
Mean:
15.264665232867896
Variance:
34.66452572836923
Standard Deviation:
5.887658764599832
One Standard Deviation Above the Mean:
21.15232399746773
Three Standard Deviations Above the Mean
32.927641526667394
One Standard Deviation Below the Mean:
9.377006468268064
Three Standard Deviations Below the Mean:
-2.398311060931599

To better understand the data, I needed to know what the mean was, and when data started to be really significant for my analysis. What amount of people without internet was exceptionally high? What amount indicated that the county had very good internet access? With the mean at about 15% of the county without internet access, it started to paint a better picture of what it meant to have exceptionally high or low access. It is worth noting that the dataset has high variance. While this is not necessarily a statistical problem, it does show the inequity of internet access in the United States.

In [11]:
df_transform = df_transform[df_transform.percent_no_internet < 32.928]
df_transform.head(5)
Out[11]:
county state GEOID lon lat P_total P_white P_black P_asian P_native P_hawaiian P_others P_below_middle_school P_some_high_school P_high_school_equivalent P_some_college P_bachelor_and_above P_below_poverty median_age gini_index median_household_income median_rent_per_income percent_no_internet FIPS
0 Anchorage Municipality AK 05000US02020 -149.274354 61.177549 298192.0 184841.0 16102.0 27142.0 23916.0 7669.0 7935.0 2234.0 8196.0 44804.0 66162.0 70713.0 18302.0 33.0 0.4018 85634.0 28.0 6.593887 02020
1 Fairbanks North Star Borough AK 05000US02090 -146.599867 64.690832 100605.0 75501.0 4385.0 3875.0 7427.0 503.0 2357.0 924.0 1527.0 14725.0 24570.0 19257.0 9580.0 30.6 0.3756 77328.0 25.6 12.102458 02090
2 Matanuska-Susitna Borough AK 05000US02170 -149.407974 62.182173 104365.0 86314.0 1019.0 1083.0 5455.0 141.0 325.0 337.0 2755.0 21071.0 28472.0 12841.0 9893.0 34.2 0.4351 69332.0 29.6 11.156575 02170
3 Baldwin County AL 05000US01003 -87.746067 30.659218 208563.0 180484.0 18821.0 914.0 1383.0 0.0 1469.0 3245.0 10506.0 41822.0 46790.0 43547.0 23375.0 42.4 0.4498 56732.0 29.3 17.868167 01003
4 Calhoun County AL 05000US01015 -85.822513 33.771706 114611.0 NaN NaN NaN NaN NaN NaN 2455.0 8853.0 24761.0 26625.0 12909.0 18193.0 39.1 0.4692 41687.0 24.8 23.464932 01015
In [18]:
trace0 = go.Box(
    y=df_transform.percent_no_internet,
    name='Mean & SD',
    marker=dict(
        color='rgb(10, 140, 208)',
    ),
    boxmean='sd'
)
data = [trace0]

layout = go.Layout(
    title='Households Without Internet Boxplot',
    
    yaxis=dict(
        title='Percent of Households with No Internet',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    )
)

fig = dict(data=data, layout=layout)
iplot(fig, filename='boxplot')
In [19]:
#for reference I've calculated the mean, and standard deviations, so we can learn what is statistically significant.
#this still includes outliers like Apache County, AZ
mean = df_transform.percent_no_internet.mean()
print('Statistics for Percent of Household Without Internet Access:')
print('Mean:')
print(mean)
variance = df_transform.percent_no_internet.var()
print('Variance:')
print(variance)
std_dev = df_transform.percent_no_internet.std()
print('Standard Deviation:')
print(std_dev)
one_above = (mean + std_dev)
print('One Standard Deviation Above the Mean:')
print(one_above)
three_above = (mean + (std_dev * 3))
print('Three Standard Deviations Above the Mean')
print(three_above)
one_below = (mean - std_dev)
print('One Standard Deviation Below the Mean:')
print(one_below)
three_below = (mean - (std_dev * 3))
print('Three Standard Deviations Below the Mean:')
print(three_below)
Statistics for Percent of Household Without Internet Access:
Mean:
15.017807864600915
Variance:
28.302371741718005
Standard Deviation:
5.319997344145766
One Standard Deviation Above the Mean:
20.33780520874668
Three Standard Deviations Above the Mean
30.977799897038214
One Standard Deviation Below the Mean:
9.69781052045515
Three Standard Deviations Below the Mean:
-0.942184167836384

With exceptional outliers outside of two standard deviations above the mean excluded, I wanted to see how this impacted the statistics. While the changes were not drastic, I still think it was worth excluding counties where more than 32% of households do not have internet since there may be some extenuating circumstances that are not representative of the dataset.

In [20]:
x = df_transform.percent_no_internet
data = [go.Histogram(x=x)]

iplot(data, filename='basic histogram')

Since the data was fairly normally distributed, I did not normalize the data any further, and worked with it as is.

In [21]:
data = [go.Bar(x=df_transform.state,
            y=df_transform.percent_no_internet)]
layout = go.Layout(
    title='Households Without Internet by State',
    xaxis=dict(
        title='States',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    ),
    yaxis=dict(
        title='Percent of Households with No Internet',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    )
)

fig = dict(data=data, layout=layout)
iplot(fig, filename='basic-bar')

This is one last graph for initial understanding. States such as Arizona and Texas have high rates of households without internet access. Texas is very interesting given it's high level of variance. The mean access is low, but there are clearly areas that have many households without access. It is worth noting that a few counties in Texas were excluded from the data since they may be outliers.

Analytic Questions

After doing an initial analysis of the mean and understanding what communities have many households without internet, I was left to think about why this might be. This does indeed show that we have a wide variance of internet access in the United States, but also a dearth of data in this area. Given the data available, I think income, poverty rates and education might be related to especially high or low rates of households without internet access. To best analyze it, I'll run linear regressions where appropriate, so that it might give us clues as to what to expect from data that we may not have through this dataset, given it's natural limitations.

How does a county's median income impact it's rate of internet access?

In [12]:
xi = df_transform.median_household_income
y = df_transform.percent_no_internet

# Generated linear fit
slope, intercept, r_value, p_value, std_err = stats.linregress(xi,y)
line = slope*xi+intercept

# Creating the dataset, and generating the plot
trace1 = go.Scatter(
                  x=xi,
                  y=y,
                  text = df_transform.county +', ' + df_transform.state,
                  mode='markers',
                  marker=go.Marker(color='rgb(255, 127, 14)'),
                  name='Data'
                  )

trace2 = go.Scatter(
                  x=xi,
                  y=line,
                  mode='lines',
                  marker=go.Marker(color='rgb(31, 119, 180)'),
                  name='Fit'
                  )

annotation = go.Annotation(
                  x=10,# Does this stay constant?
                  y=80000,#Does this stay constant?
                  text='$R^2 = 0.9551,\\Y = 0.716X + 19.18$',
                  showarrow=False,
                  font=go.Font(size=16)
                  )
data = [trace1, trace2]

layout = go.Layout(
    title='Percentage of Households Without Internet and Median Income',
    xaxis=dict(
        title='Median Income',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    ),
    yaxis=dict(
        title='Percentage of Households Without Internet',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    )
)


fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='Linear-Fit-in-python')
print('Slope:')
print(slope)
print('Amount Households Without Internet Decreases Per $10,000' )
print(slope * 10000)
/anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:426: DeprecationWarning:

plotly.graph_objs.Marker is deprecated.
Please replace it with one of the following more specific types
  - plotly.graph_objs.scatter.Marker
  - plotly.graph_objs.histogram.selected.Marker
  - etc.


/anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:318: DeprecationWarning:

plotly.graph_objs.Font is deprecated.
Please replace it with one of the following more specific types
  - plotly.graph_objs.layout.Font
  - plotly.graph_objs.layout.hoverlabel.Font
  - etc.


/anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:144: DeprecationWarning:

plotly.graph_objs.Annotation is deprecated.
Please replace it with one of the following more specific types
  - plotly.graph_objs.layout.Annotation
  - plotly.graph_objs.layout.scene.Annotation


Slope:
-0.0002544184703634143
Amount Households Without Internet Decreases Per $10,000
-2.544184703634143

This was a very clear relationship in the data. With the dependent variable, Percent of Households with No Internet, on the Y-Axis, we can see that as a county's median household income increases, it's number of households without internet decreases. I also ran a linear regression, which showed good fit and affirmed the inverse relationship. This is logical since households will have more disposable income that they can use to subscribe to broadband internet.

Does the median age of a county increase or decrease the percentage of homes without internet?

In [24]:
trace0 = go.Scatter(
    x = df_transform.median_age,
    y = df_transform.percent_no_internet,
    name = 'Above',
    text = df_transform.county +', ' + df_transform.state,
    mode = 'markers',
    marker = dict(
        size = 10,
        color = 'rgba(63, 191, 191, .8)',
        line = dict(
            width = 2,
            color = 'rgb(0, 0, 0)'
        )
    )
)
data = [trace0]

layout = go.Layout(
    title='Percentage of Households Without Internet and Median Age',
    xaxis=dict(
        title='Median Age',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    ),
    yaxis=dict(
        title='Percent of Households with No Internet',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    )
)
fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='basic-scatter')

This dataset did show a strong relationship between median age and percentage of households without internet. I had expected that as age increased, households without internet may increase since older generations may have less interest in using broadband internet.

Does education level influence the percentage of houses without internet?

I thought that education level might have a good deal of influence on the percentage of houses without internet in a particular county. First, I created a percentage of the county that had a particular education level by using the population total (P_total) and the number of people with each education level for the counties for which I had data. I ran an initial scatterplot to see if what I posited might be correct.

In [25]:
percent_below_middle = (df_transform.P_below_middle_school/df_transform.P_total *100)
percent_some_hs = (df_transform.P_some_high_school/df_transform.P_total * 100)
percent_hs = (df_transform.P_high_school_equivalent/df_transform.P_total * 100)
percent_some_col = (df_transform.P_some_college/df_transform.P_total * 100)
percent_bach_or_above = (df_transform.P_bachelor_and_above/df_transform.P_total * 100)
trace0 = go.Scatter(
    x = percent_below_middle,
    y = df_transform.percent_no_internet,
    name = 'Below Middle School',
    text = df_transform.county +', ' + df_transform.state,
    mode = 'markers',
    marker = dict(
        size = 10,
        color = 'rgb(86, 244, 66, .8)',
        line = dict(
            width = 2,
            color = 'rgb(0, 0, 0)'
        )
    )
)

trace1 = go.Scatter(
    x = percent_some_hs,
    y = df_transform.percent_no_internet,
    name = 'Some High School',
    text = df_transform.county +', ' + df_transform.state,
    mode = 'markers',
    marker = dict(
        size = 10,
        color = 'rgb(65, 244, 223, .8)',
        line = dict(
            width = 2,
        )
    )
)

trace2 = go.Scatter(
    x = percent_hs,
    y = df_transform.percent_no_internet,
    name = 'High School Degree/Equivalent',
    text = df_transform.county +', ' + df_transform.state,
    mode = 'markers',
    marker = dict(
        size = 10,
        color = 'rgb(202, 65, 244, .8)',
        line = dict(
            width = 2,
        )
    )
)
trace3 = go.Scatter(
    x = percent_some_col,
    y = df_transform.percent_no_internet,
    name = 'Some College',
    text = df_transform.county +', ' + df_transform.state,
    mode = 'markers',
    marker = dict(
        size = 10,
        color = 'rgb(244, 178, 65, .8)',
        line = dict(
            width = 2,
        )
    )
)
trace4 = go.Scatter(
    x = percent_bach_or_above,
    y = df_transform.percent_no_internet,
    name = 'Bachelor and Above',
    text = df_transform.county +', ' + df_transform.state,
    mode = 'markers',
    marker = dict(
        size = 10,
        color = 'rgb(241, 244, 65, .8)',
        line = dict(
            width = 2,
        )
    )
)
data = [trace0, trace1, trace2, trace3, trace4]

layout = go.Layout(
    title='Education Level and Percent No Internet',
    xaxis=dict(
        title='Percentage of People at Education Level',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    ),
    yaxis=dict(
        title='% Households Without Internet',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    )
)
fig = dict(data=data, layout=layout)
iplot(fig, filename='styled-scatter')

Education does appear to have an impact on internet access. There are strong relationships between bachelor's degrees, and people who have not obtained a high school degree, or completed middle school. I have run regressions on those scatterplots to make sure that they fit well with a linear regression. This could be problematic in the future because it could contribute to a household's inability to progress in the education system, since a student's lack of internet access can be detrimental to their educational progress according to this article from Pew Research.

In [26]:
percent_below_middle = (df_transform.P_below_middle_school/df_transform.P_total *100)
xi = percent_below_middle
y = df_transform.percent_no_internet

# Generated linear fit
mask = ~np.isnan(xi) & ~np.isnan(y)
slope, intercept, r_value, p_value, std_err = stats.linregress(xi[mask], y[mask])
line = slope*xi+intercept

# Creating the dataset, and generating the plot
trace1 = go.Scatter(
                  x=xi,
                  y=y,
                  text = df_transform.county +', ' + df_transform.state,
                  mode='markers',
                  marker=go.Marker(color='rgb(86, 244, 66, .8)'),
                  name='Data'
                  )

trace2 = go.Scatter(
                  x=xi,
                  y=line,
                  mode='lines',
                  marker=go.Marker(color='rgb(31, 119, 180)'),
                  name='Fit'
                  )


data = [trace1, trace2]

layout = go.Layout(
    title='Percentage of Households Without Internet and Percentage Who Did Not Complete Middle School',
    xaxis=dict(
        title='Percentage of People Who Did Not Complete Middle School',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    ),
    yaxis=dict(
        title='Percent of Households Without Internet',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    )
)


fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='Linear-Fit-in-python')
/anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:426: DeprecationWarning:

plotly.graph_objs.Marker is deprecated.
Please replace it with one of the following more specific types
  - plotly.graph_objs.scatter.Marker
  - plotly.graph_objs.histogram.selected.Marker
  - etc.


This did not correlate as strongly as I thought it would. However, it does indicate that generally as the percentage of people who did not complete middle school may increase the likelihood that the percentage of households without internet will increase.

In [28]:
percent_some_hs = (df_transform.P_some_high_school/df_transform.P_total * 100)
xi = percent_some_hs
y = df_transform.percent_no_internet

# Generated linear fit
mask = ~np.isnan(xi) & ~np.isnan(y)
slope, intercept, r_value, p_value, std_err = stats.linregress(xi[mask], y[mask])
line = slope*xi+intercept

# Creating the dataset, and generating the plot
trace1 = go.Scatter(
                  x=xi,
                  y=y,
                  text = df_transform.county +', ' + df_transform.state,
                  mode='markers',
                  marker=go.Marker(color='rgb(113, 21, 211, .8)'),
                  name='Data'
                  )

trace2 = go.Scatter(
                  x=xi,
                  y=line,
                  mode='lines',
                  marker=go.Marker(color='rgb(21, 210, 33)'),
                  name='Fit'
                  )


data = [trace1, trace2]

layout = go.Layout(
    title='Percentage of Households Without Internet and Percentage Who Did Not Complete High School',
    xaxis=dict(
        title='Percentage of People Who Did Not Complete High School',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    ),
    yaxis=dict(
        title='Percent of Households Without Internet',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    )
)


fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='Linear-Fit-in-python')
print('Mean of People Who Did Not Complete High School:')
print(percent_some_hs.mean())
/anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:426: DeprecationWarning:

plotly.graph_objs.Marker is deprecated.
Please replace it with one of the following more specific types
  - plotly.graph_objs.scatter.Marker
  - plotly.graph_objs.histogram.selected.Marker
  - etc.


Mean of People Who Did Not Complete High School:
4.7644457401609355
Slope:
2.018361916016112

This graph shows a direct relationship between people who have some high school education, and the percentage of the county without internet access. The scatterplot is tightly arranged around the linear regression, and could be helpful for data prediction and analysis as we consider counties that have higher populations of people who did not complete high school. Since the mean is about 4%, counties that have population data, but no data regarding internet access could be analyzed further. Until more data is collected we can't be sure, but it's a strong possibility that counties that have more that 4% of people who did not complete high school would have less internet access.

In [13]:
percent_bach_or_above = (df_transform.P_bachelor_and_above/df_transform.P_total * 100)
xi = percent_bach_or_above
y = df_transform.percent_no_internet

# Generated linear fit
mask = ~np.isnan(xi) & ~np.isnan(y)
slope, intercept, r_value, p_value, std_err = stats.linregress(xi[mask], y[mask])
line = slope*xi+intercept

# Creating the dataset, and generating the plot
trace1 = go.Scatter(
                  x=xi,
                  y=y,
                  text = df_transform.county +', ' + df_transform.state,
                  mode='markers',
                  marker=go.Marker(color='rgb(205, 244, 65, .8)'),
                  name='Data'
                  )

trace2 = go.Scatter(
                  x=xi,
                  y=line,
                  mode='lines',
                  marker=go.Marker(color='rgb(54, 56, 49)'),
                  name='Fit'
                  )


data = [trace1, trace2]

layout = go.Layout(
    title='Percentage of Households Without Internet and Percentage of People With Bachelors Degrees and Above',
    xaxis=dict(
        title='Percentage of People Who Have A Bachelors Degree or Above',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    ),
    yaxis=dict(
        title='Percent of Households Without Internet',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    )
)


fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='Linear-Fit-in-python')
print('Mean Percentage of Those with Bachelors Degrees or More Advanced Degrees:')
print(percent_bach_or_above.mean())
/anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:426: DeprecationWarning:

plotly.graph_objs.Marker is deprecated.
Please replace it with one of the following more specific types
  - plotly.graph_objs.scatter.Marker
  - plotly.graph_objs.histogram.selected.Marker
  - etc.


Mean Percentage of Those with Bachelors Degrees or More Advanced Degrees:
19.75133498857654

This shows a strong relationship with good linear fit. As the percentage of people who have a bachelor's degree or more advanced degree increases, the percentage of the county without internet decreases. Nearly 40% of the county with the lowest percentage of households without internet, Douglas County in Colorado, have a bachelor's degree or higher. Perhaps this might be related to colleges or universities being nearby, which typically have excellent internet access and generally pays employees well, so that they might be able to afford broadband internet access. The mean of this data is about 19.6%, which could help with predictive data in the future. In counties where more than 19.6% of the population has a bachelor's degree could have exceptionally good internet, and may have structures in place that are worth analyzing. While it may just be good infrastructure, there may be other factors that could be replicated in places that do not have high rates of internet access.

Does the percentage of a person's income spent on rent have any relationship with internet access?

In [14]:
# Create a trace
trace = go.Scatter(
    x = df_transform.median_rent_per_income,
    y = df_transform.percent_no_internet,
    name = 'Below',
    text = df_transform.county +', ' + df_transform.state,
    mode = 'markers',
    marker = dict(
        size = 10,
        color = 'rgb(206, 16, 168, .8)',
        line = dict(
            width = 2,
        )
    )
)

data = [trace]

layout = go.Layout(
    title='Percentage of Households Compared to Percent Spent on Rent',
    xaxis=dict(
        title='Percent of Income Spent on Rent',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    ),
    yaxis=dict(
        title='Percent of Households with No Internet',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    )
)

fig = dict(data=data, layout=layout)
iplot(fig, filename='styled-scatter')

In short, it does not appear so. I had expected this data to show a stronger, inverse relationship. However, percent of income spent on rent did not have a great impact on the percent of households without internet. However, this still does make sense as it is reasonable that people may move to a more expensive area if they make more money, rather than spend less of their income in an area that is less expensive.

Does the rate of poverty in a county increase the percentage of households without internet?

I first converted the column P_below_poverty to a percentage, so that the data could be properly analyzed. I then generated a scatterplot so I could assess the relationship I posited.

In [15]:
percent_below_poverty = (df_transform.P_below_poverty/df_transform.P_total * 100)
xi = percent_below_poverty
y = df_transform.percent_no_internet

# Generated linear fit
mask = ~np.isnan(xi) & ~np.isnan(y)
slope, intercept, r_value, p_value, std_err = stats.linregress(xi[mask], y[mask])
line = slope*xi+intercept

# Creating the dataset, and generating the plot
trace1 = go.Scatter(
                  x=xi,
                  y=y,
                  text = df_transform.county +', ' + df_transform.state,
                  mode='markers',
                  marker=go.Marker(color='rgb(15, 216, 193, .1)'),
                  name='Data'
                  )

trace2 = go.Scatter(
                  x=xi,
                  y=line,
                  mode='lines',
                  marker=go.Marker(color='rgb(31, 119, 180)'),
                  name='Fit'
                  )

annotation = go.Annotation(
                  x=3.5,# Does this stay constant?
                  y=23.5,#Does this stay constant?
                  text='$R^2 = 0.9551,\\Y = 0.716X + 19.18$',
                  showarrow=False,
                  font=go.Font(size=16)
                  )
data = [trace1, trace2]

layout = go.Layout(
    title='Percentage of Households Without Internet and Percentage of Households Below Poverty',
    xaxis=dict(
        title='Percent of Households Below Poverty',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    ),
    yaxis=dict(
        title='Percent of County Without Internet',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    )
)


fig = go.Figure(data=data, layout=layout)
iplot(fig, filename='Linear-Fit-in-python')
/anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:426: DeprecationWarning:

plotly.graph_objs.Marker is deprecated.
Please replace it with one of the following more specific types
  - plotly.graph_objs.scatter.Marker
  - plotly.graph_objs.histogram.selected.Marker
  - etc.


/anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:318: DeprecationWarning:

plotly.graph_objs.Font is deprecated.
Please replace it with one of the following more specific types
  - plotly.graph_objs.layout.Font
  - plotly.graph_objs.layout.hoverlabel.Font
  - etc.


/anaconda3/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:144: DeprecationWarning:

plotly.graph_objs.Annotation is deprecated.
Please replace it with one of the following more specific types
  - plotly.graph_objs.layout.Annotation
  - plotly.graph_objs.layout.scene.Annotation


It does appear that the percentage of households below poverty did relate positively to the percentage of households without internet. This makes sense, since someone who earns an income below the rate of poverty would reasonably not be able to afford expensive internet plans. This could have an impact in the future, since we are very internet reliant as a society currently and will only grow to be more so. This may be an important consideration, if this data analysis holds true, for public policy decisions in the future. Just as we have assistance for people who have an income below a certain point for food, housing, and heat, internet may just become that important to society and may be something we need to adjust for in future social protections.

Does the percentage of people of a certain race impact internet access rates?

In [16]:
#data manipulation
percent_pop_white = (df_transform.P_white/df_transform.P_total * 100)
percent_pop_black = (df_transform.P_black/df_transform.P_total * 100)
percent_pop_asian = (df_transform.P_asian/df_transform.P_total * 100)
percent_pop_hawaiian = (df_transform.P_hawaiian/df_transform.P_total * 100)
percent_pop_natamer = (df_transform.P_native/df_transform.P_total *100)
percent_pop_other = (df_transform.P_others/df_transform.P_total *100)

#traces
trace0 = go.Scatter(
    x = percent_pop_white,
    y = df_transform.percent_no_internet,
    name = 'White',
    text = df_transform.county +', ' + df_transform.state,
    mode = 'markers',
    marker = dict(
        size = 10,
        color = 'rgb(58, 124, 153, .8)',
        line = dict(
            width = 2,
            color = 'rgb(0, 0, 0)'
        )
    )
)

trace1 = go.Scatter(
    x = percent_pop_black,
    y = df_transform.percent_no_internet,
    name = 'Black',
    text = df_transform.county +', ' + df_transform.state,
    mode = 'markers',
    marker = dict(
        size = 10,
        color = 'rgb(254, 127, 45, .8)',
        line = dict(
            width = 2,
        )
    )
)

trace2 = go.Scatter(
    x = percent_pop_asian,
    y = df_transform.percent_no_internet,
    name = 'Asian',
    text = df_transform.county +', ' + df_transform.state,
    mode = 'markers',
    marker = dict(
        size = 10,
        color = 'rgb(202, 69, 252, .8)',
        line = dict(
            width = 2,
        )
    )
)
trace3 = go.Scatter(
    x = percent_pop_hawaiian,
    y = df_transform.percent_no_internet,
    name = 'Hawaiian',
    text = df_transform.county +', ' + df_transform.state,
    mode = 'markers',
    marker = dict(
        size = 10,
        color = 'rgb(87, 156, 1135, .8)',
        line = dict(
            width = 2,
        )
    )
)
trace4 = go.Scatter(
    x = percent_pop_natamer,
    y = df_transform.percent_no_internet,
    name = 'Native American',
    text = df_transform.county +', ' + df_transform.state,
    mode = 'markers',
    marker = dict(
        size = 10,
        color = 'rgb(241, 244, 65, .8)',
        line = dict(
            width = 2,
        )
    )
)
trace5 = go.Scatter(
    x = percent_pop_other,
    y = df_transform.percent_no_internet,
    name = 'Other',
    text = df_transform.county +', ' + df_transform.state,
    mode = 'markers',
    marker = dict(
        size = 10,
        color = 'rgb(116, 193, 65, .8)',
        line = dict(
            width = 2,
        )
    )
)

data = [trace0, trace1, trace2, trace3, trace4, trace5]

layout = go.Layout(
    title='Race and Percent No Internet',
    xaxis=dict(
        title='# of People by Race',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    ),
    yaxis=dict(
        title='Percent of Households with No Internet',
        titlefont=dict(
            family='Courier New, monospace',
            size=18,
            color='#7f7f7f'
        )
    )
)
fig = dict(data=data, layout=layout)
iplot(fig, filename='styled-scatter')

I did not anticipate a strong relationship here, and this seems to be supported by the data. I generated the percentage of people by each race, and related that to the percentage of households without internet. This would not be helpful in assessing this data, or trying to predict where internet access rates might be higher or lower. Additionaly, these columns were some of the more incomplete ones in the dataset. There may be stronger relationships here, but I am unable to analyze it with the current data available.

Areas for Further Study

To build and fill the Chloropleth map from the start of the data, I had to find a dataset with FIPS codes, and then clean it so that it could be used here. This included adding some leading zeroes, and concatenating two of the columns before finally merging it with the dataframe I used for the rest of the dataset.

In [17]:
df_1 = pd.read_csv('~/Desktop/Python Exercises/us_fips_codes.csv')
df_1.head(5)
Out[17]:
State County Name FIPS State FIPS County
0 Alabama Autauga 1 1
1 Alabama Baldwin 1 3
2 Alabama Barbour 1 5
3 Alabama Bibb 1 7
4 Alabama Blount 1 9
In [18]:
print(df_1.dtypes)
State          object
County Name    object
FIPS State      int64
FIPS County     int64
dtype: object
In [20]:
df_1['FIPS State'] = df_1['FIPS State'].apply(lambda x: '{0:0>2}'.format(x))
df_1
Out[20]:
State County Name FIPS State FIPS County
0 Alabama Autauga 01 1
1 Alabama Baldwin 01 3
2 Alabama Barbour 01 5
3 Alabama Bibb 01 7
4 Alabama Blount 01 9
5 Alabama Bullock 01 11
6 Alabama Butler 01 13
7 Alabama Calhoun 01 15
8 Alabama Chambers 01 17
9 Alabama Cherokee 01 19
10 Alabama Chilton 01 21
11 Alabama Choctaw 01 23
12 Alabama Clarke 01 25
13 Alabama Clay 01 27
14 Alabama Cleburne 01 29
15 Alabama Coffee 01 31
16 Alabama Colbert 01 33
17 Alabama Conecuh 01 35
18 Alabama Coosa 01 37
19 Alabama Covington 01 39
20 Alabama Crenshaw 01 41
21 Alabama Cullman 01 43
22 Alabama Dale 01 45
23 Alabama Dallas 01 47
24 Alabama De Kalb 01 49
25 Alabama Elmore 01 51
26 Alabama Escambia 01 53
27 Alabama Etowah 01 55
28 Alabama Fayette 01 57
29 Alabama Franklin 01 59
30 Alabama Geneva 01 61
31 Alabama Greene 01 63
32 Alabama Hale 01 65
33 Alabama Henry 01 67
34 Alabama Houston 01 69
35 Alabama Jackson 01 71
36 Alabama Jefferson 01 73
37 Alabama Lamar 01 75
38 Alabama Lauderdale 01 77
39 Alabama Lawrence 01 79
40 Alabama Lee 01 81
41 Alabama Limestone 01 83
42 Alabama Lowndes 01 85
43 Alabama Macon 01 87
44 Alabama Madison 01 89
45 Alabama Marengo 01 91
46 Alabama Marion 01 93
47 Alabama Marshall 01 95
48 Alabama Mobile 01 97
49 Alabama Monroe 01 99
50 Alabama Montgomery 01 101
51 Alabama Morgan 01 103
52 Alabama Perry 01 105
53 Alabama Pickens 01 107
54 Alabama Pike 01 109
55 Alabama Randolph 01 111
56 Alabama Russell 01 113
57 Alabama St Clair 01 115
58 Alabama Shelby 01 117
59 Alabama Sumter 01 119
60 Alabama Talladega 01 121
61 Alabama Tallapoosa 01 123
62 Alabama Tuscaloosa 01 125
63 Alabama Walker 01 127
64 Alabama Washington 01 129
65 Alabama Wilcox 01 131
66 Alabama Winston 01 133
67 Alaska Aleutians East 02 13
68 Alaska Aleutians West 02 16
69 Alaska Anchorage 02 20
70 Alaska Bethel 02 50
71 Alaska Bristol Bay 02 60
72 Alaska Denali 02 68
73 Alaska Dillingham 02 70
74 Alaska Fairbanks North Star 02 90
75 Alaska Haines 02 100
76 Alaska Juneau 02 110
77 Alaska Kenai Peninsula 02 122
78 Alaska Ketchikan Gateway 02 130
79 Alaska Kodiak Island 02 150
80 Alaska Lake and Peninsula 02 164
81 Alaska Matanuska Susitna 02 170
82 Alaska Nome 02 180
83 Alaska North Slope 02 185
84 Alaska Northwest Arctic 02 188
85 Alaska Prince Wales Ketchikan 02 201
86 Alaska Sitka 02 220
87 Alaska Skagway Hoonah Angoon 02 232
88 Alaska Southeast Fairbanks 02 240
89 Alaska Valdez Cordova 02 261
90 Alaska Wade Hampton 02 270
91 Alaska Wrangell Petersburg 02 280
92 Alaska Yakutat 02 282
93 Alaska Yukon Koyukuk 02 290
94 Arizona Apache 04 1
95 Arizona Cochise 04 3
96 Arizona Coconino 04 5
97 Arizona Gila 04 7
98 Arizona Graham 04 9
99 Arizona Greenlee 04 11
100 Arizona La Paz 04 12
101 Arizona Maricopa 04 13
102 Arizona Mohave 04 15
103 Arizona Navajo 04 17
104 Arizona Pima 04 19
105 Arizona Pinal 04 21
106 Arizona Santa Cruz 04 23
107 Arizona Yavapai 04 25
108 Arizona Yuma 04 27
109 Arkansas Arkansas 05 1
110 Arkansas Ashley 05 3
111 Arkansas Baxter 05 5
112 Arkansas Benton 05 7
113 Arkansas Boone 05 9
114 Arkansas Bradley 05 11
115 Arkansas Calhoun 05 13
116 Arkansas Carroll 05 15
117 Arkansas Chicot 05 17
118 Arkansas Clark 05 19
119 Arkansas Clay 05 21
120 Arkansas Cleburne 05 23
121 Arkansas Cleveland 05 25
122 Arkansas Columbia 05 27
123 Arkansas Conway 05 29
124 Arkansas Craighead 05 31
125 Arkansas Crawford 05 33
126 Arkansas Crittenden 05 35
127 Arkansas Cross 05 37
128 Arkansas Dallas 05 39
129 Arkansas Desha 05 41
130 Arkansas Drew 05 43
131 Arkansas Faulkner 05 45
132 Arkansas Franklin 05 47
133 Arkansas Fulton 05 49
134 Arkansas Garland 05 51
135 Arkansas Grant 05 53
136 Arkansas Greene 05 55
137 Arkansas Hempstead 05 57
138 Arkansas Hot Spring 05 59
139 Arkansas Howard 05 61
140 Arkansas Independence 05 63
141 Arkansas Izard 05 65
142 Arkansas Jackson 05 67
143 Arkansas Jefferson 05 69
144 Arkansas Johnson 05 71
145 Arkansas Lafayette 05 73
146 Arkansas Lawrence 05 75
147 Arkansas Lee 05 77
148 Arkansas Lincoln 05 79
149 Arkansas Little River 05 81
150 Arkansas Logan 05 83
151 Arkansas Lonoke 05 85
152 Arkansas Madison 05 87
153 Arkansas Marion 05 89
154 Arkansas Miller 05 91
155 Arkansas Mississippi 05 93
156 Arkansas Monroe 05 95
157 Arkansas Nevada 05 99
158 Arkansas Newton 05 101
159 Arkansas Ouachita 05 103
160 Arkansas Perry 05 105
161 Arkansas Phillips 05 107
162 Arkansas Pike 05 109
163 Arkansas Poinsett 05 111
164 Arkansas Polk 05 113
165 Arkansas Pope 05 115
166 Arkansas Prairie 05 117
167 Arkansas Pulaski 05 119
168 Arkansas Randolph 05 121
169 Arkansas St Francis 05 123
170 Arkansas Saline 05 125
171 Arkansas Scott 05 127
172 Arkansas Searcy 05 129
173 Arkansas Sebastian 05 131
174 Arkansas Sevier 05 133
175 Arkansas Sharp 05 135
176 Arkansas Stone 05 137
177 Arkansas Union 05 139
178 Arkansas Van Buren 05 141
179 Arkansas Washington 05 143
180 Arkansas White 05 145
181 Arkansas Woodruff 05 147
182 Arkansas Yell 05 149
183 California Alameda 06 1
184 California Alpine 06 3
185 California Amador 06 5
186 California Butte 06 7
187 California Calaveras 06 9
188 California Colusa 06 11
189 California Contra Costa 06 13
190 California Del Norte 06 15
191 California El Dorado 06 17
192 California Fresno 06 19
193 California Glenn 06 21
194 California Humboldt 06 23
195 California Imperial 06 25
196 California Inyo 06 27
197 California Kern 06 29
198 California Kings 06 31
199 California Lake 06 33
200 California Lassen 06 35
201 California Los Angeles 06 37
202 California Madera 06 39
203 California Marin 06 41
204 California Mariposa 06 43
205 California Mendocino 06 45
206 California Merced 06 47
207 California Modoc 06 49
208 California Mono 06 51
209 California Monterey 06 53
210 California Napa 06 55
211 California Nevada 06 57
212 California Orange 06 59
213 California Placer 06 61
214 California Plumas 06 63
215 California Riverside 06 65
216 California Sacramento 06 67
217 California San Benito 06 69
218 California San Bernardino 06 71
219 California San Diego 06 73
220 California San Francisco 06 75
221 California San Joaquin 06 77
222 California San Luis Obispo 06 79
223 California San Mateo 06 81
224 California Santa Barbara 06 83
225 California Santa Clara 06 85
226 California Santa Cruz 06 87
227 California Shasta 06 89
228 California Sierra 06 91
229 California Siskiyou 06 93
230 California Solano 06 95
231 California Sonoma 06 97
232 California Stanislaus 06 99
233 California Sutter 06 101
234 California Tehama 06 103
235 California Trinity 06 105
236 California Tulare 06 107
237 California Tuolumne 06 109
238 California Ventura 06 111
239 California Yolo 06 113
240 California Yuba 06 115
241 Colorado Adams 08 1
242 Colorado Alamosa 08 3
243 Colorado Arapahoe 08 5
244 Colorado Archuleta 08 7
245 Colorado Baca 08 9
246 Colorado Bent 08 11
247 Colorado Boulder 08 13
248 Colorado Broomfield 08 14
249 Colorado Chaffee 08 15
250 Colorado Cheyenne 08 17
251 Colorado Clear Creek 08 19
252 Colorado Conejos 08 21
253 Colorado Costilla 08 23
254 Colorado Crowley 08 25
255 Colorado Custer 08 27
256 Colorado Delta 08 29
257 Colorado Denver 08 31
258 Colorado Dolores 08 33
259 Colorado Douglas 08 35
260 Colorado Eagle 08 37
261 Colorado Elbert 08 39
262 Colorado El Paso 08 41
263 Colorado Fremont 08 43
264 Colorado Garfield 08 45
265 Colorado Gilpin 08 47
266 Colorado Grand 08 49
267 Colorado Gunnison 08 51
268 Colorado Hinsdale 08 53
269 Colorado Huerfano 08 55
270 Colorado Jackson 08 57
271 Colorado Jefferson 08 59
272 Colorado Kiowa 08 61
273 Colorado Kit Carson 08 63
274 Colorado Lake 08 65
275 Colorado La Plata 08 67
276 Colorado Larimer 08 69
277 Colorado Las Animas 08 71
278 Colorado Lincoln 08 73
279 Colorado Logan 08 75
280 Colorado Mesa 08 77
281 Colorado Mineral 08 79
282 Colorado Moffat 08 81
283 Colorado Montezuma 08 83
284 Colorado Montrose 08 85
285 Colorado Morgan 08 87
286 Colorado Otero 08 89
287 Colorado Ouray 08 91
288 Colorado Park 08 93
289 Colorado Phillips 08 95
290 Colorado Pitkin 08 97
291 Colorado Prowers 08 99
292 Colorado Pueblo 08 101
293 Colorado Rio Blanco 08 103
294 Colorado Rio Grande 08 105
295 Colorado Routt 08 107
296 Colorado Saguache 08 109
297 Colorado San Juan 08 111
298 Colorado San Miguel 08 113
299 Colorado Sedgwick 08 115
300 Colorado Summit 08 117
301 Colorado Teller 08 119
302 Colorado Washington 08 121
303 Colorado Weld 08 123
304 Colorado Yuma 08 125
305 Connecticut Fairfield 09 1
306 Connecticut Hartford 09 3
307 Connecticut Litchfield 09 5
308 Connecticut Middlesex 09 7
309 Connecticut New Haven 09 9
310 Connecticut New London 09 11
311 Connecticut Tolland 09 13
312 Connecticut Windham 09 15
313 Delaware Kent 10 1
314 Delaware New Castle 10 3
315 Delaware Sussex 10 5
316 District of Columbia District of Columbia 11 1
317 District of Columbia Montgomery 11 31
318 Florida Alachua 12 1
319 Florida Baker 12 3
320 Florida Bay 12 5
321 Florida Bradford 12 7
322 Florida Brevard 12 9
323 Florida Broward 12 11
324 Florida Calhoun 12 13
325 Florida Charlotte 12 15
326 Florida Citrus 12 17
327 Florida Clay 12 19
328 Florida Collier 12 21
329 Florida Columbia 12 23
330 Florida De Soto 12 27
331 Florida Dixie 12 29
332 Florida Duval 12 31
333 Florida Escambia 12 33
334 Florida Flagler 12 35
335 Florida Franklin 12 37
336 Florida Gadsden 12 39
337 Florida Gilchrist 12 41
338 Florida Glades 12 43
339 Florida Gulf 12 45
340 Florida Hamilton 12 47
341 Florida Hardee 12 49
342 Florida Hendry 12 51
343 Florida Hernando 12 53
344 Florida Highlands 12 55
345 Florida Hillsborough 12 57
346 Florida Holmes 12 59
347 Florida Indian River 12 61
348 Florida Jackson 12 63
349 Florida Jefferson 12 65
350 Florida Lafayette 12 67
351 Florida Lake 12 69
352 Florida Lee 12 71
353 Florida Leon 12 73
354 Florida Levy 12 75
355 Florida Liberty 12 77
356 Florida Madison 12 79
357 Florida Manatee 12 81
358 Florida Marion 12 83
359 Florida Martin 12 85
360 Florida Miami-Dade 12 86
361 Florida Monroe 12 87
362 Florida Nassau 12 89
363 Florida Okaloosa 12 91
364 Florida Okeechobee 12 93
365 Florida Orange 12 95
366 Florida Osceola 12 97
367 Florida Palm Beach 12 99
368 Florida Pasco 12 101
369 Florida Pinellas 12 103
370 Florida Polk 12 105
371 Florida Putnam 12 107
372 Florida St Johns 12 109
373 Florida St Lucie 12 111
374 Florida Santa Rosa 12 113
375 Florida Sarasota 12 115
376 Florida Seminole 12 117
377 Florida Sumter 12 119
378 Florida Suwannee 12 121
379 Florida Taylor 12 123
380 Florida Union 12 125
381 Florida Volusia 12 127
382 Florida Wakulla 12 129
383 Florida Walton 12 131
384 Florida Washington 12 133
385 Georgia Appling 13 1
386 Georgia Atkinson 13 3
387 Georgia Bacon 13 5
388 Georgia Baker 13 7
389 Georgia Baldwin 13 9
390 Georgia Banks 13 11
391 Georgia Barrow 13 13
392 Georgia Bartow 13 15
393 Georgia Ben Hill 13 17
394 Georgia Berrien 13 19
395 Georgia Bibb 13 21
396 Georgia Bleckley 13 23
397 Georgia Brantley 13 25
398 Georgia Brooks 13 27
399 Georgia Bryan 13 29
400 Georgia Bulloch 13 31
401 Georgia Burke 13 33
402 Georgia Butts 13 35
403 Georgia Calhoun 13 37
404 Georgia Camden 13 39
405 Georgia Candler 13 43
406 Georgia Carroll 13 45
407 Georgia Catoosa 13 47
408 Georgia Charlton 13 49
409 Georgia Chatham 13 51
... ... ... ... ...
2732 Texas Somervell 48 425
2733 Texas Starr 48 427
2734 Texas Stephens 48 429
2735 Texas Sterling 48 431
2736 Texas Stonewall 48 433
2737 Texas Sutton 48 435
2738 Texas Swisher 48 437
2739 Texas Tarrant 48 439
2740 Texas Taylor 48 441
2741 Texas Terrell 48 443
2742 Texas Terry 48 445
2743 Texas Throckmorton 48 447
2744 Texas Titus 48 449
2745 Texas Tom Green 48 451
2746 Texas Travis 48 453
2747 Texas Trinity 48 455
2748 Texas Tyler 48 457
2749 Texas Upshur 48 459
2750 Texas Upton 48 461
2751 Texas Uvalde 48 463
2752 Texas Val Verde 48 465
2753 Texas Van Zandt 48 467
2754 Texas Victoria 48 469
2755 Texas Walker 48 471
2756 Texas Waller 48 473
2757 Texas Ward 48 475
2758 Texas Washington 48 477
2759 Texas Webb 48 479
2760 Texas Wharton 48 481
2761 Texas Wheeler 48 483
2762 Texas Wichita 48 485
2763 Texas Wilbarger 48 487
2764 Texas Willacy 48 489
2765 Texas Williamson 48 491
2766 Texas Wilson 48 493
2767 Texas Winkler 48 495
2768 Texas Wise 48 497
2769 Texas Wood 48 499
2770 Texas Yoakum 48 501
2771 Texas Young 48 503
2772 Texas Zapata 48 505
2773 Texas Zavala 48 507
2774 Utah Beaver 49 1
2775 Utah Box Elder 49 3
2776 Utah Cache 49 5
2777 Utah Carbon 49 7
2778 Utah Daggett 49 9
2779 Utah Davis 49 11
2780 Utah Duchesne 49 13
2781 Utah Emery 49 15
2782 Utah Garfield 49 17
2783 Utah Grand 49 19
2784 Utah Iron 49 21
2785 Utah Juab 49 23
2786 Utah Kane 49 25
2787 Utah Millard 49 27
2788 Utah Morgan 49 29
2789 Utah Piute 49 31
2790 Utah Rich 49 33
2791 Utah Salt Lake 49 35
2792 Utah San Juan 49 37
2793 Utah Sanpete 49 39
2794 Utah Sevier 49 41
2795 Utah Summit 49 43
2796 Utah Tooele 49 45
2797 Utah Uintah 49 47
2798 Utah Utah 49 49
2799 Utah Wasatch 49 51
2800 Utah Washington 49 53
2801 Utah Wayne 49 55
2802 Utah Weber 49 57
2803 Vermont Addison 50 1
2804 Vermont Bennington 50 3
2805 Vermont Caledonia 50 5
2806 Vermont Chittenden 50 7
2807 Vermont Essex 50 9
2808 Vermont Franklin 50 11
2809 Vermont Grand Isle 50 13
2810 Vermont Lamoille 50 15
2811 Vermont Orange 50 17
2812 Vermont Orleans 50 19
2813 Vermont Rutland 50 21
2814 Vermont Washington 50 23
2815 Vermont Windham 50 25
2816 Vermont Windsor 50 27
2817 Virginia Accomack 51 1
2818 Virginia Albemarle 51 3
2819 Virginia Alleghany 51 5
2820 Virginia Amelia 51 7
2821 Virginia Amherst 51 9
2822 Virginia Appomattox 51 11
2823 Virginia Arlington 51 13
2824 Virginia Augusta 51 15
2825 Virginia Bath 51 17
2826 Virginia Bedford 51 19
2827 Virginia Bland 51 21
2828 Virginia Botetourt 51 23
2829 Virginia Brunswick 51 25
2830 Virginia Buchanan 51 27
2831 Virginia Buckingham 51 29
2832 Virginia Campbell 51 31
2833 Virginia Caroline 51 33
2834 Virginia Carroll 51 35
2835 Virginia Charles City 51 36
2836 Virginia Charlotte 51 37
2837 Virginia Chesterfield 51 41
2838 Virginia Clarke 51 43
2839 Virginia Craig 51 45
2840 Virginia Culpeper 51 47
2841 Virginia Cumberland 51 49
2842 Virginia Dickenson 51 51
2843 Virginia Dinwiddie 51 53
2844 Virginia Essex 51 57
2845 Virginia Fairfax 51 59
2846 Virginia Fauquier 51 61
2847 Virginia Floyd 51 63
2848 Virginia Fluvanna 51 65
2849 Virginia Franklin 51 67
2850 Virginia Frederick 51 69
2851 Virginia Giles 51 71
2852 Virginia Gloucester 51 73
2853 Virginia Goochland 51 75
2854 Virginia Grayson 51 77
2855 Virginia Greene 51 79
2856 Virginia Greensville 51 81
2857 Virginia Halifax 51 83
2858 Virginia Hanover 51 85
2859 Virginia Henrico 51 87
2860 Virginia Henry 51 89
2861 Virginia Highland 51 91
2862 Virginia Isle of Wight 51 93
2863 Virginia James City 51 95
2864 Virginia King and Queen 51 97
2865 Virginia King George 51 99
2866 Virginia King William 51 101
2867 Virginia Lancaster 51 103
2868 Virginia Lee 51 105
2869 Virginia Loudoun 51 107
2870 Virginia Louisa 51 109
2871 Virginia Lunenburg 51 111
2872 Virginia Madison 51 113
2873 Virginia Mathews 51 115
2874 Virginia Mecklenburg 51 117
2875 Virginia Middlesex 51 119
2876 Virginia Montgomery 51 121
2877 Virginia Nelson 51 125
2878 Virginia New Kent 51 127
2879 Virginia Northampton 51 131
2880 Virginia Northumberland 51 133
2881 Virginia Nottoway 51 135
2882 Virginia Orange 51 137
2883 Virginia Page 51 139
2884 Virginia Patrick 51 141
2885 Virginia Pittsylvania 51 143
2886 Virginia Powhatan 51 145
2887 Virginia Prince Edward 51 147
2888 Virginia Prince George 51 149
2889 Virginia Prince William 51 153
2890 Virginia Pulaski 51 155
2891 Virginia Rappahannock 51 157
2892 Virginia Richmond 51 159
2893 Virginia Roanoke 51 161
2894 Virginia Rockbridge 51 163
2895 Virginia Rockingham 51 165
2896 Virginia Russell 51 167
2897 Virginia Scott 51 169
2898 Virginia Shenandoah 51 171
2899 Virginia Smyth 51 173
2900 Virginia Southampton 51 175
2901 Virginia Spotsylvania 51 177
2902 Virginia Stafford 51 179
2903 Virginia Surry 51 181
2904 Virginia Sussex 51 183
2905 Virginia Tazewell 51 185
2906 Virginia Warren 51 187
2907 Virginia Washington 51 191
2908 Virginia Westmoreland 51 193
2909 Virginia Wise 51 195
2910 Virginia Wythe 51 197
2911 Virginia York 51 199
2912 Virginia Alexandria City 51 510
2913 Virginia Bedford City 51 515
2914 Virginia Bristol City 51 520
2915 Virginia Buena Vista City 51 530
2916 Virginia Charlottesville City 51 540
2917 Virginia Chesapeake City 51 550
2918 Virginia Clifton Forge City 51 560
2919 Virginia Colonial Heights City 51 570
2920 Virginia Covington City 51 580
2921 Virginia Danville City 51 590
2922 Virginia Emporia City 51 595
2923 Virginia Fairfax City 51 600
2924 Virginia Falls Church City 51 610
2925 Virginia Franklin City 51 620
2926 Virginia Fredericksburg City 51 630
2927 Virginia Galax City 51 640
2928 Virginia Hampton City 51 650
2929 Virginia Harrisonburg City 51 660
2930 Virginia Hopewell City 51 670
2931 Virginia Lexington City 51 678
2932 Virginia Lynchburg City 51 680
2933 Virginia Manassas City 51 683
2934 Virginia Manassas Park City 51 685
2935 Virginia Martinsville City 51 690
2936 Virginia Newport News City 51 700
2937 Virginia Norfolk City 51 710
2938 Virginia Norton City 51 720
2939 Virginia Petersburg City 51 730
2940 Virginia Poquoson City 51 735
2941 Virginia Portsmouth City 51 740
2942 Virginia Radford 51 750
2943 Virginia Richmond City 51 760
2944 Virginia Roanoke City 51 770
2945 Virginia Salem City 51 775
2946 Virginia South Boston City 51 780
2947 Virginia Staunton City 51 790
2948 Virginia Suffolk City 51 800
2949 Virginia Virginia Beach City 51 810
2950 Virginia Waynesboro City 51 820
2951 Virginia Williamsburg City 51 830
2952 Virginia Winchester City 51 840
2953 Washington Adams 53 1
2954 Washington Asotin 53 3
2955 Washington Benton 53 5
2956 Washington Chelan 53 7
2957 Washington Clallam 53 9
2958 Washington Clark 53 11
2959 Washington Columbia 53 13
2960 Washington Cowlitz 53 15
2961 Washington Douglas 53 17
2962 Washington Ferry 53 19
2963 Washington Franklin 53 21
2964 Washington Garfield 53 23
2965 Washington Grant 53 25
2966 Washington Grays Harbor 53 27
2967 Washington Island 53 29
2968 Washington Jefferson 53 31
2969 Washington King 53 33
2970 Washington Kitsap 53 35
2971 Washington Kittitas 53 37
2972 Washington Klickitat 53 39
2973 Washington Lewis 53 41
2974 Washington Lincoln 53 43
2975 Washington Mason 53 45
2976 Washington Okanogan 53 47
2977 Washington Pacific 53 49
2978 Washington Pend Oreille 53 51
2979 Washington Pierce 53 53
2980 Washington San Juan 53 55
2981 Washington Skagit 53 57
2982 Washington Skamania 53 59
2983 Washington Snohomish 53 61
2984 Washington Spokane 53 63
2985 Washington Stevens 53 65
2986 Washington Thurston 53 67
2987 Washington Wahkiakum 53 69
2988 Washington Walla Walla 53 71
2989 Washington Whatcom 53 73
2990 Washington Whitman 53 75
2991 Washington Yakima 53 77
2992 West Virginia Barbour 54 1
2993 West Virginia Berkeley 54 3
2994 West Virginia Boone 54 5
2995 West Virginia Braxton 54 7
2996 West Virginia Brooke 54 9
2997 West Virginia Cabell 54 11
2998 West Virginia Calhoun 54 13
2999 West Virginia Clay 54 15
3000 West Virginia Doddridge 54 17
3001 West Virginia Fayette 54 19
3002 West Virginia Gilmer 54 21
3003 West Virginia Grant 54 23
3004 West Virginia Greenbrier 54 25
3005 West Virginia Hampshire 54 27
3006 West Virginia Hancock 54 29
3007 West Virginia Hardy 54 31
3008 West Virginia Harrison 54 33
3009 West Virginia Jackson 54 35
3010 West Virginia Jefferson 54 37
3011 West Virginia Kanawha 54 39
3012 West Virginia Lewis 54 41
3013 West Virginia Lincoln 54 43
3014 West Virginia Logan 54 45
3015 West Virginia McDowell 54 47
3016 West Virginia Marion 54 49
3017 West Virginia Marshall 54 51
3018 West Virginia Mason 54 53
3019 West Virginia Mercer 54 55
3020 West Virginia Mineral 54 57
3021 West Virginia Mingo 54 59
3022 West Virginia Monongalia 54 61
3023 West Virginia Monroe 54 63
3024 West Virginia Morgan 54 65
3025 West Virginia Nicholas 54 67
3026 West Virginia Ohio 54 69
3027 West Virginia Pendleton 54 71
3028 West Virginia Pleasants 54 73
3029 West Virginia Pocahontas 54 75
3030 West Virginia Preston 54 77
3031 West Virginia Putnam 54 79
3032 West Virginia Raleigh 54 81
3033 West Virginia Randolph 54 83
3034 West Virginia Ritchie 54 85
3035 West Virginia Roane 54 87
3036 West Virginia Summers 54 89
3037 West Virginia Taylor 54 91
3038 West Virginia Tucker 54 93
3039 West Virginia Tyler 54 95
3040 West Virginia Upshur 54 97
3041 West Virginia Wayne 54 99
3042 West Virginia Webster 54 101
3043 West Virginia Wetzel 54 103
3044 West Virginia Wirt 54 105
3045 West Virginia Wood 54 107
3046 West Virginia Wyoming 54 109
3047 Wisconsin Adams 55 1
3048 Wisconsin Ashland 55 3
3049 Wisconsin Barron 55 5
3050 Wisconsin Bayfield 55 7
3051 Wisconsin Brown 55 9
3052 Wisconsin Buffalo 55 11
3053 Wisconsin Burnett 55 13
3054 Wisconsin Calumet 55 15
3055 Wisconsin Chippewa 55 17
3056 Wisconsin Clark 55 19
3057 Wisconsin Columbia 55 21
3058 Wisconsin Crawford 55 23
3059 Wisconsin Dane 55 25
3060 Wisconsin Dodge 55 27
3061 Wisconsin Door 55 29
3062 Wisconsin Douglas 55 31
3063 Wisconsin Dunn 55 33
3064 Wisconsin Eau Claire 55 35
3065 Wisconsin Florence 55 37
3066 Wisconsin Fond Du Lac 55 39
3067 Wisconsin Forest 55 41
3068 Wisconsin Grant 55 43
3069 Wisconsin Green 55 45
3070 Wisconsin Green Lake 55 47
3071 Wisconsin Iowa 55 49
3072 Wisconsin Iron 55 51
3073 Wisconsin Jackson 55 53
3074 Wisconsin Jefferson 55 55
3075 Wisconsin Juneau 55 57
3076 Wisconsin Kenosha 55 59
3077 Wisconsin Kewaunee 55 61
3078 Wisconsin La Crosse 55 63
3079 Wisconsin Lafayette 55 65
3080 Wisconsin Langlade 55 67
3081 Wisconsin Lincoln 55 69
3082 Wisconsin Manitowoc 55 71
3083 Wisconsin Marathon 55 73
3084 Wisconsin Marinette 55 75
3085 Wisconsin Marquette 55 77
3086 Wisconsin Menominee 55 78
3087 Wisconsin Milwaukee 55 79
3088 Wisconsin Monroe 55 81
3089 Wisconsin Oconto 55 83
3090 Wisconsin Oneida 55 85
3091 Wisconsin Outagamie 55 87
3092 Wisconsin Ozaukee 55 89
3093 Wisconsin Pepin 55 91
3094 Wisconsin Pierce 55 93
3095 Wisconsin Polk 55 95
3096 Wisconsin Portage 55 97
3097 Wisconsin Price 55 99
3098 Wisconsin Racine 55 101
3099 Wisconsin Richland 55 103
3100 Wisconsin Rock 55 105
3101 Wisconsin Rusk 55 107
3102 Wisconsin St Croix 55 109
3103 Wisconsin Sauk 55 111
3104 Wisconsin Sawyer 55 113
3105 Wisconsin Shawano 55 115
3106 Wisconsin Sheboygan 55 117
3107 Wisconsin Taylor 55 119
3108 Wisconsin Trempealeau 55 121
3109 Wisconsin Vernon 55 123
3110 Wisconsin Vilas 55 125
3111 Wisconsin Walworth 55 127
3112 Wisconsin Washburn 55 129
3113 Wisconsin Washington 55 131
3114 Wisconsin Waukesha 55 133
3115 Wisconsin Waupaca 55 135
3116 Wisconsin Waushara 55 137
3117 Wisconsin Winnebago 55 139
3118 Wisconsin Wood 55 141
3119 Wyoming Albany 56 1
3120 Wyoming Big Horn 56 3
3121 Wyoming Campbell 56 5
3122 Wyoming Carbon 56 7
3123 Wyoming Converse 56 9
3124 Wyoming Crook 56 11
3125 Wyoming Fremont 56 13
3126 Wyoming Goshen 56 15
3127 Wyoming Hot Springs 56 17
3128 Wyoming Johnson 56 19
3129 Wyoming Laramie 56 21
3130 Wyoming Lincoln 56 23
3131 Wyoming Natrona 56 25
3132 Wyoming Niobrara 56 27
3133 Wyoming Park 56 29
3134 Wyoming Platte 56 31
3135 Wyoming Sheridan 56 33
3136 Wyoming Sublette 56 35
3137 Wyoming Sweetwater 56 37
3138 Wyoming Teton 56 39
3139 Wyoming Uinta 56 41
3140 Wyoming Washakie 56 43
3141 Wyoming Weston 56 45

3142 rows × 4 columns

In [21]:
df_1['FIPS County'] = df_1['FIPS County'].apply(lambda x: '{0:0>3}'.format(x))
df_1.head(5)
Out[21]:
State County Name FIPS State FIPS County
0 Alabama Autauga 01 001
1 Alabama Baldwin 01 003
2 Alabama Barbour 01 005
3 Alabama Bibb 01 007
4 Alabama Blount 01 009
In [22]:
df_1.columns = ['state', 'county_name', 'fips_state', 'fips_county']
df_1.head(5)
Out[22]:
state county_name fips_state fips_county
0 Alabama Autauga 01 001
1 Alabama Baldwin 01 003
2 Alabama Barbour 01 005
3 Alabama Bibb 01 007
4 Alabama Blount 01 009
In [23]:
df_1['FIPS'] = df_1["fips_state"] + df_1["fips_county"]
df_1.head(5)
Out[23]:
state county_name fips_state fips_county FIPS
0 Alabama Autauga 01 001 01001
1 Alabama Baldwin 01 003 01003
2 Alabama Barbour 01 005 01005
3 Alabama Bibb 01 007 01007
4 Alabama Blount 01 009 01009
In [24]:
df_1[df_1.index.duplicated()]
Out[24]:
state county_name fips_state fips_county FIPS
In [25]:
print('FIPS' in df_1)
True
In [26]:
print(df_1.dtypes)
state          object
county_name    object
fips_state     object
fips_county    object
FIPS           object
dtype: object
In [27]:
df_map = df[['county', 'state', 'GEOID','percent_no_internet']].copy()
In [28]:
def func(row):
    
    return row.GEOID[-5:]

df_map['FIPS'] = df.apply(func,axis=1)
#pd.set_option('display.max_columns', 24)
df_map.head(5)
Out[28]:
county state GEOID percent_no_internet FIPS
0 Anchorage Municipality AK 05000US02020 6.593887 02020
1 Fairbanks North Star Borough AK 05000US02090 12.102458 02090
2 Matanuska-Susitna Borough AK 05000US02170 11.156575 02170
3 Baldwin County AL 05000US01003 17.868167 01003
4 Calhoun County AL 05000US01015 23.464932 01015
In [29]:
df_map['percent_no_internet'] = df_map['percent_no_internet'].astype(float)
df_map.head(5)
Out[29]:
county state GEOID percent_no_internet FIPS
0 Anchorage Municipality AK 05000US02020 6.593887 02020
1 Fairbanks North Star Borough AK 05000US02090 12.102458 02090
2 Matanuska-Susitna Borough AK 05000US02170 11.156575 02170
3 Baldwin County AL 05000US01003 17.868167 01003
4 Calhoun County AL 05000US01015 23.464932 01015
In [30]:
print(df_map.dtypes)
county                  object
state                   object
GEOID                   object
percent_no_internet    float64
FIPS                    object
dtype: object
In [31]:
df_clean = pd.merge(df_map, df_1, on='FIPS', how='outer')
df_clean.head(5)
Out[31]:
county state_x GEOID percent_no_internet FIPS state_y county_name fips_state fips_county
0 Anchorage Municipality AK 05000US02020 6.593887 02020 Alaska Anchorage 02 020
1 Fairbanks North Star Borough AK 05000US02090 12.102458 02090 Alaska Fairbanks North Star 02 090
2 Matanuska-Susitna Borough AK 05000US02170 11.156575 02170 Alaska Matanuska Susitna 02 170
3 Baldwin County AL 05000US01003 17.868167 01003 Alabama Baldwin 01 003
4 Calhoun County AL 05000US01015 23.464932 01015 Alabama Calhoun 01 015
In [34]:
df_clean["percent_no_internet"].fillna(0, inplace=True)
In [33]:
#county chloropleth map of households without internet
colorscale = ["#b8babc","#ebf3fb","#deebf7","#d2e3f3","#c6dbef","#b3d2e9","#9ecae1",
              "#85bcdb","#6baed6","#57a0ce","#4292c6","#3082be","#2171b5","#1361a9",
              "#08519c","#0b4083","#08306b"]
endpts = list(np.linspace(0, 55, len(colorscale) - 1))
fips = df_clean['FIPS'].tolist()
values = df_clean['percent_no_internet'].tolist()

fig = ff.create_choropleth(
    fips=fips, values=values,
    binning_endpoints=endpts,
    colorscale=colorscale,
    show_state_data=False,
    show_hover=True, centroid_marker={'opacity': 0},
    asp=2.9, title='Percentage of People with No Internet',
    legend_title='% without internet'
    
)
iplot(fig, filename='choropleth_full_usa')

Overall, this dataset is incomplete at the moment, but may be expanded upon with the 2018 update of the data. This was a valuable exploration, as we can possbily use the data here to train a model and generate some predictive data. As I learn more about Machine Learning, this seems very feasible to project the possible percentage of households without internet based upon median income, education level, and rate of poverty in the county. Since there are three factors for comparison, it stands to reason that the data produced would be accurate.

It is logical but supported by data that areas with a higher median income, lower poverty rate, and higher education level tend to have fewer internet deserts. This can help focus on areas that may have particularly good internet solutions, or areas that need it most, but were not able to be included here. I would encourage focus on the areas excluded from the data due to statistical insignificance (i.e. most of the midwest). This could be an area of further analysis with census data and public policy, especially in light of recent net neutrality decisions. In this US News article, there are many proposed solutions to promote equity for the most vulnerable members of the households discussed here.

Internet access, and data protection may become some of the most important areas of public policy, and there are many viable solutions here such as providing students with wireless hotspots or inexpensive chromebooks with which they can do homework. There are also solutions in place currently, like a low-cost internet package from Comcast for $10 per month for qualifying low-income customers. However, this may not be a widely known option. Solutions such as these may start to correct some of the sources of the internet inequity such as income and education.

Alternative solutions might include municipal broadband for instance, Chattanooga, Tennessee is widely cited as a forerunner in the municipal broadband movement. It offers low-cost, high-speed internet service that is well-rated by outside agencies as shown here in this Tech Crunch article. This model might be of consideration to many areas that are experiencing low rates of access, as it also will create jobs and improve access for children and adults to things like education, work and more. Longitudinal research on places like Chattanooga will be crucial to determine viability over time given cost, maintenance and satisfaction.